Collaborative Multi-Robot Search and Rescue: Planning ... · perception. This is, to the best of...

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1 Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception and Active Vision Jorge Pe˜ na Queralta 1 , Jussi Taipalmaa 2 , Bilge Can Pullinen 2 , Victor Kathan Sarker 1 , Tuan Nguyen Gia 1 , Hannu Tenhunen 1 , Moncef Gabbouj 2 , Jenni Raitoharju 2,3 , Tomi Westerlund 1 1 Turku Intelligent Embedded and Robotic Systems, University of Turku, Finland Email: 1 {jopequ, vikasar, tunggi, tovewe}@utu.fi 2 Department of Computing Sciences, Tampere University, Finland Email: 2 {jussi.taipalmaa, bilge.canpullinen, moncef.gabbouj}@tuni.fi 3 Programme for Environmental Information, Finnish Environment Institute, Jyv¨ askyl¨ a, Finland Email: jenni.raitoharju@environment.fi Abstract—Search and rescue (SAR) operations can take sig- nificant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This pa- per provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi- agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim- to-real transferability of existing methods, awareness of victims’ conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area. Index Terms—Robotics, search and rescue (SAR), multi-robot systems (MRS), machine learning (ML), deep learning (DL), active perception, active vision, multi-agent perception. I. I NTRODUCTION Autonomous or teleoperated robots have been playing in- creasingly important roles in civil applications in recent years. Across the different civil domains where robots can support human operators, one of the areas where they can have more impact is in search and rescue (SAR) operations. In particular, multi-robot systems have the potential to signif- icantly improve the efficiency of SAR personnel with faster response time [1], [2], support in hazardous environments [3]– [5], or providing real-time mapping and monitoring of the area where an incident has occurred [6], [7], among other possibilities. This paper presents a literature review of multi- robot systems (MRS) for SAR operations with a focus on coordination and perception algorithms and, specifically, how these two perspectives can be bridged through different active perception approaches. This algorithmic view of MRS for SAR is preceded in the paper by a system perspective of robotic SAR systems and their operational environments, some of which are illustrated in Fig. 1. The important abbreviations utilized throughout the paper are listed in Table I. The literature contains multiple survey papers that review robotics for SAR operations. Multi-UAV systems for civil applications (where SAR applications are a subset) are re- viewed in [8] from the point of view of communication. A classification of technological trends and sensing modalities in UAVs for civil applications is available in [9]. Focusing on SAR robotics, UAVs for SAR operations are reviewed in [10], with a classification in terms of (i) sensing, (ii) system- level definitions, and (iii) operational environments. A study of MRS for SAR operations in [11] focuses on task allocation algorithms, communication modalities, and human-robot inter- action for both homogeneous and heterogeneous multi-robot systems. While autonomous robots are being increasingly adopted for SAR missions, current levels of autonomy and safety of robotic systems only allow for full autonomy in the search part, but not for rescue, where human operators need to intervene [12]. In general, the literature on multi-robot SAR operations with some degree of autonomy is rather sparse, with most results based on simulations or simplified scenarios [13]. The main objective of this survey is to provide a com- prehensive introduction to multi-robot SAR systems from two complimentary perspectives: (i) control and coordination algorithms, and (ii) deep learning models for online percep- tion. This review thus aims at providing an entry point to researchers from either of the two fields looking for a global view at MRS design for SAR operations. To that end, the paper starts with an overview of the most significant projects and competitions in the field, together with a system-level perspective (Fig. 2a. The survey is, in turn, closed with an introduction to multi-robot active perception, as the key piece to bridge control and perception design (Fig. 2b). Finally, we discuss the most important research directions and open challenges giving insight into key aspects in MRS design for

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Page 1: Collaborative Multi-Robot Search and Rescue: Planning ... · perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different

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Collaborative Multi-Robot Search and Rescue:Planning, Coordination, Perception and Active Vision

Jorge Pena Queralta1, Jussi Taipalmaa2, Bilge Can Pullinen2, Victor Kathan Sarker1, Tuan Nguyen Gia1,Hannu Tenhunen1, Moncef Gabbouj2, Jenni Raitoharju2,3, Tomi Westerlund1

1Turku Intelligent Embedded and Robotic Systems, University of Turku, FinlandEmail: 1{jopequ, vikasar, tunggi, tovewe}@utu.fi

2Department of Computing Sciences, Tampere University, FinlandEmail: 2{jussi.taipalmaa, bilge.canpullinen, moncef.gabbouj}@tuni.fi

3Programme for Environmental Information, Finnish Environment Institute, Jyvaskyla, FinlandEmail: [email protected]

Abstract—Search and rescue (SAR) operations can take sig-nificant advantage from supporting autonomous or teleoperatedrobots and multi-robot systems. These can aid in mapping andsituational assessment, monitoring and surveillance, establishingcommunication networks, or searching for victims. This pa-per provides a review of multi-robot systems supporting SARoperations, with system-level considerations and focusing onthe algorithmic perspectives for multi-robot coordination andperception. This is, to the best of our knowledge, the firstsurvey paper to cover (i) heterogeneous SAR robots in differentenvironments, (ii) active perception in multi-robot systems, while(iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss themost significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims’conditions, coordination and interoperability in heterogeneousmulti-robot systems, and active perception. The different topicsin the survey are put in the context of the different challengesand constraints that various types of robots (ground, aerial,surface, or underwater) encounter in different SAR environments(maritime, urban, wilderness, or other post-disaster scenarios).The objective of this survey is to serve as an entry point to thevarious aspects of multi-robot SAR systems to researchers inboth the machine learning and control fields by giving a globaloverview of the main approaches being taken in the SAR roboticsarea.

Index Terms—Robotics, search and rescue (SAR), multi-robotsystems (MRS), machine learning (ML), deep learning (DL),active perception, active vision, multi-agent perception.

I. INTRODUCTION

Autonomous or teleoperated robots have been playing in-creasingly important roles in civil applications in recent years.Across the different civil domains where robots can supporthuman operators, one of the areas where they can havemore impact is in search and rescue (SAR) operations. Inparticular, multi-robot systems have the potential to signif-icantly improve the efficiency of SAR personnel with fasterresponse time [1], [2], support in hazardous environments [3]–[5], or providing real-time mapping and monitoring of thearea where an incident has occurred [6], [7], among otherpossibilities. This paper presents a literature review of multi-robot systems (MRS) for SAR operations with a focus on

coordination and perception algorithms and, specifically, howthese two perspectives can be bridged through different activeperception approaches. This algorithmic view of MRS for SARis preceded in the paper by a system perspective of roboticSAR systems and their operational environments, some ofwhich are illustrated in Fig. 1. The important abbreviationsutilized throughout the paper are listed in Table I.

The literature contains multiple survey papers that reviewrobotics for SAR operations. Multi-UAV systems for civilapplications (where SAR applications are a subset) are re-viewed in [8] from the point of view of communication. Aclassification of technological trends and sensing modalitiesin UAVs for civil applications is available in [9]. Focusingon SAR robotics, UAVs for SAR operations are reviewedin [10], with a classification in terms of (i) sensing, (ii) system-level definitions, and (iii) operational environments. A studyof MRS for SAR operations in [11] focuses on task allocationalgorithms, communication modalities, and human-robot inter-action for both homogeneous and heterogeneous multi-robotsystems. While autonomous robots are being increasinglyadopted for SAR missions, current levels of autonomy andsafety of robotic systems only allow for full autonomy in thesearch part, but not for rescue, where human operators needto intervene [12]. In general, the literature on multi-robot SARoperations with some degree of autonomy is rather sparse, withmost results based on simulations or simplified scenarios [13].

The main objective of this survey is to provide a com-prehensive introduction to multi-robot SAR systems fromtwo complimentary perspectives: (i) control and coordinationalgorithms, and (ii) deep learning models for online percep-tion. This review thus aims at providing an entry point toresearchers from either of the two fields looking for a globalview at MRS design for SAR operations. To that end, thepaper starts with an overview of the most significant projectsand competitions in the field, together with a system-levelperspective (Fig. 2a. The survey is, in turn, closed with anintroduction to multi-robot active perception, as the key pieceto bridge control and perception design (Fig. 2b). Finally,we discuss the most important research directions and openchallenges giving insight into key aspects in MRS design for

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(a) UAVs can replace or support helicopters in maritime SAR tolocate people or transport lifeguard floats before USVs arrive.

(b) UAVs (and UGVs) can aid in assessing damage and monitor-ing earthquake-hit areas.

(c) Gazebo environment with hector quadrotor model and Rvizvisualization tool. The simulator and robot were utilized inthe RoboCup2019 Rescue Virtual Robot League.

(d) Illustration of the simulation environment for the DARPASubT Virtual Challenge with two USVs and two UAVs.

Fig. 1: Selection of real and simulated SAR scenarios (mar-itime, urban, wilderness, underground), with heteroge-neous multi-robot systems.

TABLE I: List of important abbreviations used throughout thepaper in alphabetical order.

Abbreviation Definition

AT Avalanche TransmitterCNN Convolutional Neural NetworkCSAT Cooperative Search, Acquisition and TrackingCT Cooperative TrackingDL Deep LearningDRL Deep Reinforcement LearningEKF Extended Kalman FilterGNSS Global Navigation Satellite SystemGPS Global Positioning SystemIPP Informative Path PlanningMANET Mobile Ad-hoc NetworkML Machine LearningMPC Model Predictive ControlMRS Multi-Robot SystemNP Nondeterministic Polynomial TimeRGB-D RGB + DepthRL Reinforcement LearningROS Robot Operating SystemRSSI Received Signal Strength IndicatorSAR Search and RecueSLAM Simultaneous Localization and MappingUAV Unmanned Aerial VehicleUGV Unmanned Ground VehicleUSAR Urban Search and RescueUSV Unmanned Surface VehicleUUV Unmanned Underwater VehicleUWB Ultra-WidebandWiSAR Wilderness Search and Rescue

RAS operations, including shared autonomy, human conditionawareness, multi-robot active perception, and challenges inheterogeneous MRS. This is, to the best of our knowledge,the first survey to cover simultaneously both coordination andcontrol algorithms, and machine-learning-based perception,and the first one to review active perception approaches inMRS for SAR operations.

The remainder of this paper is organized as follows: Sec-tion II describes some of the most relevant projects in SARrobotics, with an emphasis on those considering multi-robotsystems. Some of the most important competitions in SARrobotics are also presented in this section. In Section III, wepresent a system view on SAR robotic systems, describing thedifferent types of robots being utilized, particularities of SARenvironments, and different aspects for multi-robot SAR in-cluding communication and shared autonomy. Section IV fol-lows with the description of the main algorithms in multi-agentplanning and multi-robot coordination that can be applied toSAR scenarios. In Section V, we focus on machine visionand multi-agent perception from a deep learning perspective.Then, Section VI goes through the concept of active visionand delves into the integration of both coordination and plan-ning algorithms with robotic vision towards active perceptionalgorithms where the latter provides additional feedback to thecontrol loops of the former. In Section VII, we discuss openresearch questions in the field of autonomous heterogeneousmulti-robot systems for SAR operations, outlining the maindirections that current research is being directed to. Finally,Section VIII concludes this work.

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System Level Perspective of Multi-Robot SAR Systems

Equipmentand Sensors

OperationalEnvironments

HumanDetection Communication

SharedAutonomy

Section III-A Section III-B Section III-C Section III-D Section III-E

(a) Aspects of multi-robot SAR systems discussed in Section III of this paper.

Algorithmic Perspective of Multi-Robot SAR Systems

Coordination Algorithms Perception Algorithms

Path Planningand Area Coverage

Multi-Agent DecisionMaking and Planning

Segmentation andObject Detection

Multi-ModalSensor Fusion

Active Multi-AgentPerception

Section IV-A to IV-C Section IV-D to IV-F Section V-A to V-C Section V-D to V-E

Section IV Section VSection VI(b) Division of multi-robot SAR systems into separate components from an algorithmic point of view. Control, planning and

coordination algorithms are described in Section IV, while Section V reviews perception algorithms from a machine learningperspective. Section VI then puts these two views together by reviewing the works in single and multi-agent active perception.

Fig. 2: Summary of the different aspects of multi-robot SAR systems considered in this survey, where we have separated (a)system-level perspective, and (b) planning and perception algorithmic perspective.

II. INTERNATIONAL PROJECTS AND COMPETITIONS

Over the past two decades, multiple international projectshave been devoted to SAR robotics, often with the aim ofworking towards multi-robot solutions and the developmentof multi-modal sensor fusion algorithms. In this section, ourobjective is to give a general idea of the main specificobjectives that different projects have had, which in turns givesan idea of the evolution of the technology and the currentpossibilities. We thus review the most relevant internationalprojects and international competitions in SAR robotics, whichare listed in Table II. Some of the projects focus in thedevelopment of complex robotic systems that can be remotelycontrolled [14]. However, the majority of the projects considermulti-robot systems [15]–[18], and over half of the projectsconsider collaborative heterogeneous robots. In Table II, wehave described these projects from a system-level point ofview, without considering the degree of autonomy or thecontrol and perception algorithms. These latter two aspectsare described in Sections III through VI, where not only theseprojects but also other relevant works are put into a moreappropriate context.

An early approach to the design and development ofheterogeneous multi-UAV systems for cooperative activitieswas presented within the COMETS project (real-time co-ordination and control of multiple heterogeneous unmanned

aerial vehicles) [15]. In terms of human-robot collaborationfor SAR operations, one of the first EU funded projectsin SAR robotics, PeLoTe [19]–[21], designed mobile robotsfor SAR missions and developed a heterogeneous telematicsystem for cooperative (human-robot) SAR operations. Otherinternational projects designing and developing autonomousmulti-robot systems for SAR operations include the NIFTi EUproject (natural human-robot cooperation in dynamic environ-ments) [16], ICARUS (unmanned SAR) [18], [22], TRADR(long-term human-robot teaming for disaster response) [17],[23], [24], or SmokeBot (mobile robots with novel environ-mental sensors for inspection of disaster sites with low visi-bility) [25], [26]. Other projects, such as CENTAURO (robustmobility and dexterous manipulation in disaster response byfullbody telepresence in a centaur-like robot), have focused onthe development of more advanced robots that are not fullyautonomous but controlled in real-time [14].

In COMETS, the aim of the project was to design and im-plement a distributed control system for cooperative activitiesusing heterogeneous UAVs. To that end, the project researchersdeveloped a remote-controlled airship and an autonomoushelicopter and worked towards cooperative perception in real-time [6], [15], [27]. In NIFTi, both UGVs and UAVs wereutilized for autonomous navigation and mapping in harshenvironments [16]. The focus of the project was mostly on

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human-robot interaction and on distributing information forhuman operators at different layers. Similarly, in the TRADRproject, the focus was on collaborative efforts towards disasterresponse of both humans and robots [17], and on multi-robot planning [23], [24]. In particular, the results of TRARDinclude a framework for the integration of UAVs in SARmissions, from path planning to a global 3D point cloudgenerator [28]. The project continued with the foundationof the German Rescue Robotics Center at Fraunhofer FKIE,where broader research is conduced, for example, in maritimeSAR [29]. In ICARUS, project researchers developed anunmanned maritime capsule acting as a UUV, USVs, a largeUGV, and a group of UAVs for rapid deployment. Also, map-ping tools, middleware software for tactical communications,and a multi-domain robot command and control station [18].While these projects focused on the algorithmic aspects ofSAR operation, and on the design of multi-robot systems, inSmokebot the focus was on developing sensors and sensorfusion methods for harsh environments [25], [26]. A moredetailed description of some of these projects, specially thosethat started before 2017, is available in [30].

In terms of international competition and tournaments, tworelevant precedents in autonomous SAR operations are theEuropean Robotics League (ERL) Emergency Tournament,and the RoboCup Rescue League. In [31], the authors describethe details of what was the world’s first multi-domain (air,land and sea) multi-robot SAR competition. A total of 16international teams competed with tasks including (i) environ-ment reconnaissance and mapping (merging ground and aerialdata), (ii) search for missing workers outside and inside an oldbuilding, and (iii) pipe inspection with localization of leaks(on land and underwater). The RoboCup Rescue League, onthe other side, was proposed in 1999 [32]. One of the groundrobots utilized in the 2020 edition is described in [33], a full-scale rescue robot with a robot arm equipped with a gripper.

Another set of major events featuring search and rescuerobotics are the DARPA challenges. Humanoid robots [34] andhuman-robot coordination strategies [35] for SAR operationswere presented in the 2013-2015 DARPA Robotics Challenge.The DARPA Subterranean (SubT) Challenge, running in 2018-2021, has shifted the focus towards underground MRS forSAR operations, with ground robots and UAVs collaboratingin the tasks [36]. This challenge has demonstrated the ver-satility and significant increase of flexibility of heterogenousMRS [37], with robust UAV flight in inherent constrained envi-ronments [38], and ground robots able of navigating complexenvironments and long-term autonomy [39]. In 2020, due tothe Covid-19 pandemic, the challenge moved to a fully virtualedition with realistic simulation-based environments [40].

III. MULTI-ROBOT SAR: SYSTEM-LEVEL PERSPECTIVE

Robotic SAR systems can differ in multiple ways: theirintended operational environment (e.g., urban, maritime, orwilderness), the amount and type of robots involved (USVs,UAVs, UGVs, UUVs), their level of autonomy, and the waysin which humans control the robotic systems, among otherfactors. This section aims at introducing the main components

to consider when designing an MRS for SAR operations,from communication networks to the detection of victims, andconsidering also specific requirements of different operationalenvironments

A. System Requirements and Equipment UsedHere we describe the different types of SAR robots in the

literature and the most common onboard sensor suites.1) Ground robots: Two complimentary examples of ground

robots for SAR operations are introduced in [41], where bothlarge and small robots are described. Ground robots for SARmissions can be characterized among those with dexterousmanipulation capabilities and robust mobility on uneven ter-rain, such as the robot developed within the CENTAUROproject [14], smaller robots with the ability of moving throughtight spaces [41], or serpentine-like robots able of tethered op-eration across complex environments [42]. The recent DARPASubT challenge has seen the design and deployment of flexibleand robust ground units able of long-term autonomy and carryaerial units. For instance, the CTU-CRAS team, achievingthe best non-DARPA-funded performance in the tunnel track,utilized a Husky A200 (wheeled robot), two Absolem platform(tracked robot with four flippers), and six-legged PhantomXMark II platforms (crawling hexapod robots) [36].

2) Aerial robots: In terms of aerial robots, UAVs canbe quadrotors, fixed-wing aircrafts, or of other types (e.g.,blimps). A representative example of a quadrotor is availablein [28], where the authors introduce a platform for instan-taneous UAV-based 3D mapping during SAR missions. Theplatform offers a complete sensor suite. The main sensors area 16-channel laser scanner, an infrared camera for thermalmeasurements, an RGB camera, and inertial/positional sensorsfor GNSS and altitude estimation. The UAV, a DJI S1000+octocopter, is connected to a ground station on-board a firefighter command vehicle with a custom radio link capableof over 300 Mbps downlink speed at distances up to 300 m.The system is able to produce point clouds colored both byreflectance (from the laser measurements) and temperature(from the infrared camera). This suite of sensors is one ofthe most complete for UAVs, except for the lack of ultrasonicsensors. In general, however, cameras are the predominantsensors owing to their flexibility, size and weight. Examples ofautonomous quadrotors, fixed-wing and rotatory-wing vehiclesequipped with GNSS sensors and RGB cameras for search ofpeople in emergency scenarios are available in [43]–[45]. Adescription of different types of aerial SAR robots utilizedwithin the ICARIUS project is available in [46], and a surveyon UAVs for SAR operations by Grogan et al. shows thepredominance of RGB cameras as the main or only sensor inuse, without considering inertial and GNSS units [10]. Most ofthese and other works, however, assume the aerial robots movein large spaces and are not excessively constrained by envi-ronmental conditions (e.g., smoke, wind, or low-light). TheDARPA SubT challenge provides again examples of robotsbeing designed for more challenging environments. In [39],the authors present Duckiefloat, a collision-tolerant blimp forlong-term autonomy in underground environments. The CTU-CRAS team utilized quadrotors based on the F450 kit by DJI,

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Heterogeneous

Multi-Robot Systems

in Search and Rescue

UrbanSAR

WildernessSAR

MaritimeSAR

UAVs: aid in initial assessment, emergency networks, and surveillance.

UGVs: able of dexterous manipulation, full-body telepresence, and reaching to victims.

USVs: support units in flooded coastal areas and rivers.

UAVs: mapping, search of victims, monitoring, and transportation in remote areas.

UGVs: aid in underground caves and mines, searching victims, identifying hazards.

UAVs: aid in enhancing the situational awareness of surface units from the air.

USVs: main actors in transportation and reaching to victims.

UUVs: operate in harsh environments, search victims and assess underwater damages.

Fig. 3: Types of autonomous robots utilized in different SAR scenarios and their main advantages.

with a Bluefox RGB camera and ambient illumination froman LED stripe [36].

3) Surface and underwater robots: Maritime SAR opera-tions often involve both surface and underwater robots, withsupport UAVs. Descriptions of different surface robots offeringan overview of existing solutions are available in [47] and [48].Some particularities of maritime SAR robots include the useof seafloor pressure sensors, seismometers, and hydrophonefor the detection of tsunamis and earthquakes, or sensors formeasuring meteorological variables and water conditions (e.g.,temperature, salinity, depth, pH balance and concentrationsof different chemicals). Other examples include sensors forvarious liquids and substances for robots utilized in oil spillsor contaminated waters (e.g., laser fluorosensors).

4) Interoperability: A significant challenge in SARrobotics, owing to the specialization of robots in specific tasks,is interoperability. The ICARUS and DARIUS projects haveboth worked towards the integration of different unmannedvehicles or robots for SAR operations [30], [49]. Interop-erability is particularly important in heterogeneous multi-robot systems, where data from different sources needs tobe aggregated in real-time for efficient operation and fastactuation. Furthermore, because robots in SAR operationsare mostly supervised or partly teleoperated, the design of aground station is an essential piece in a complete SAR roboticssystem. This is even more critical when involving the controlof multi-robot systems. The design of a generic ground stationable to accommodate a wide variety of unmanned vehicleshas been one of the focuses of the DARIUS project [50].The approach to interoperability taken within the ICARIUSproject is described in detail in [51]. The project outcomesincluded a library for multi-robot cooperation in SAR mis-sions that assumes that the Robot Operating System (ROS)is the middleware utilized across all robots involved in themission. ROS is the de facto standard in robotics industry andresearch [52]. In [51], the authors also characterize typicalrobot roles, levels of autonomy for different types of robots,levels of interoperability, and robot capabilities.

B. Operational Environment

In this subsection, we characterize the main SAR environ-ments (urban, maritime and wilderness) and discuss how thedifferent challenges in each of these types of scenarios havebeen addressed in the literature. The main considerations aresummarized in Table III.

Maritime SAR: Search and rescue operations at sea werecharacterized by Zhao et al. in [61]. The paper emphasizes thatmaritime accidents tend to happen suddenly. Some of the mostsignificant factors are injury condition, possession of locationdevices and rescue equipment, and environmental factors suchas geographic position, wave height, water temperature, windspeed and visibility. A considerable amount of accidentshappen near the shoreline with favorable weather conditions,such as beaches during the summer. Robotic SAR systemscan be ready to act fast. For instance, Xian et al. designeda life-ring drone delivery system for aiding people near theshore [66].

The main types of autonomous robots utilized in maritimeSAR operations are USVs and UUVs [22], together withsupport UAVs [67]. Sea SAR operations are one of thescenarios where heterogeneous multi-robot systems have beenalready widely adopted [67]. A representative work on thearea, showing a heterogeneous and cooperative multi-robotsystem for SAR operations after ship accidents, was presentedby Mendoca et al. [60]. The authors proposed the utilizationof both a USV and UAV to find shipwreck survivors at sea,where the USV would carry the UAV until it arrives near theshipwreck location.

The combination of USVs and UUVs has also been widelystudied, with or without UAVs. Some of the most prominentexamples in this direction come from the euRathlon compe-tition and include solutions from the ICARUS project [48].The surface robot was first utilized to perform an autonomousassessment, mapping and survey of the area, identifying pointsof interest. Then, the underwater vehicle was deployed todetect pipe leaks and find victims underwater.

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TABLE III: Challenges and Opportunities for Autonomous Robots in different types of environments: Urban SAR [28], [42],[53]–[58], Maritime SAR [12], [22], [59]–[61], and Wilderness SAR [62]–[65].

Challenges Opportunities

MaritimeSAR

(i) Visual detection of people at sea, with potentially vast areas to search andcomparatively small targets to detect.(ii) The need for long-distance operation, with either high levels of autonomyor real-time communication in remote environments.(iii) Underwater robots often rely on tethered communication or need toresurface to share their findings.(iv) Localization and mapping underwater presents significant challenges owingto the transmission characteristics in water of light and other electromagneticwaves used in more traditional sensing methods.(v) Motion affected by marine currents, waves and limited water depths.

(i) UAVs can provide a significant improvement at seain term of situational awareness from the air, and can bedeployed on-site even from small ships.(ii) Heterogeneous multi-robot systems can aid in multi-modal coordinated search aggregating information from thedifferent perspectives (aerial, surface, underwater).(iii) Disposable or well-adapted USVs and UUVs can beutilized in harsh environments or bad weather conditionswhen SAR operations at sea are interrupted for safetyreasons.

UrbanSAR

(i) The presence of hazardous materials, radiation areas, or high temperatures.(ii) Localization and mapping of unknown, unstructured, dense and hazardousenvironments that result from disasters such as earthquakes or explosions, andin which robots are meant to operate.(iii) Navigation in narrow spaces and uneven terrain, being able to traversesmall apertures and navigate over unstable debris.(iv) Close cooperation with human operators in a potentially shared operationspace, requiring for well defined human-robot interaction models.

(i) Relieving human personnel from emotional stress andphysical threats (e.g., radiation, debris).(ii) Reducing the time for locating survivors. Mortality inUSAR scenarios raises significantly after 48 h.(iii) Assessing the structural parameters of the site andassisting on remote or semi-autonomous triage.(iv) Detecting and locating survivors and analyzing thesurrounding structures.(v) Establishing a communication link to survivors.

WildernessSAR

(i) In avalanche events, robots often need to access remote areas (long-termoperation) while in harsh weather conditions (e.g., low temperatures, low airpressure, high wind speeds).(ii) Exploration of underground mines and caves presents significant challengesfrom the point of view of long-term localization and communication.(iii) SAR operations to find people lost while hiking or climbing mountainsoften occur in the evening or at night, when visibility conditions make it morechallenging for UAVs or other robots to identify objects and people.(iv) WiSAR operations often involve tracking of a moving target, with a searcharea that expands through time.

(i) After an avalanche, areas that are hard to reach by landcan be quickly surveyed with UAVs.(ii) SAR personnel in mines or caves can rely on robots forenvironmental monitoring, mainly toxic gases, and avoidhazardous areas.(iii) UAVs equipped with thermal cameras can aid inthe search of lost hikers or climbers at night, and relaycommunication from SAR personnel.(iv) Multi-robot systems can build probabilistic maps formovable targets and revisit locations more optimally.

Urban SAR: Urban SAR scenarios include, among oth-ers, natural disasters (e.g., earthquakes), large fires, or ac-cidents involving hazardous materials.USAR robots increasethe safety of rescue personnel by reducing their exposure topotential hazards in the site and providing an initial assessmentof the situation. For instance, in [28], the authors describea heterogeneous multi-UAV system focused at providing aninitial assessment of the environment through mapping, objectdetection and annotation, and scene classifier.

Novel types of robotic systems have also been developedto better adapt to the challenges of USAR environments. Tobe able to utilize UAVs near fires, Myeong et al. presentedFAROS, a fireproof drone for USAR operations [68]. Otherrobots have taken inspiration from video scopes and fiberscopes utilized to obtain imagery from confined spaces [69],[70]. In [42], researchers participating in the ImPACT-TRCchallenge presented a thin serpentine robot platform, a longand flexible continuum robot with a length of up to 10 m anda diameter of just 50 mm, able to localize itself with visualSLAM and access collapsed buildings.

Wilderness SAR: In wilderness SAR (WiSAR) opera-tions, the literature often includes SAR in mountains [62],underground mines and caves [64], [65], [71], and forests andother rural or remote environments [43], [72], [73]. The needfor heterogeneous MRS in WiSAR environments has beenmade evident during the DARPA SubT challenge [36], [37].

One of the most common SAR operations in mountain

environments occurs in a post-avalanche scenario. In areaswith a risk of avalanches, mountaineers often carry avalanchetransmitters (AT).UAVs prepared for harsh conditions (strongwinds, high altitude and low temperatures) have been utilizedfor searching ATs [62]. In [63], an autonomous multi-UAVsystem for localizing avalanche victims was developed.

Forest environments also present significant challenges fromthe perception point of view, due to the density of theenvironments and lack of structure for path planning [74].WiSAR operations might involve tracking a moving target(a lost person), and thus the search area increases throughtime [72].

Another specific scenario that has attracted research at-tention is SAR for mining applications [64]. Two specificchallenges in SAR operations in underground environmentsare the limitations of wireless communication and the exis-tence of potentially toxic gases. Ranjan et al. have presentedan overview of wireless robotic communication networks forunderground mines [71]. The DARPA SubT challenge hasprovided an opportunity for developing novel multi-robotcommunication techniques and including the utilization ofbreadcrumb nodes [75].

C. Triage

In a scene of an accident or a natural disaster, an essentialstep once victims are found is to follow a triage protocol.Triage is the process through which victims are pre-assessed.

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In [76], the authors explored from the perspective of medicalspecialists how robots could interact with victims and performan autonomous triage. In [77], the focus was on analyzingthe potential benefits and challenges in robotics technology toassess those vital signs in an autonomous manner.

D. Shared Autonomy and Human-Swarm Interaction

In multi-robot systems and robots involving complex ma-nipulation (e.g., humanoids) with a high number of degrees offreedom, such as humanoids, the concept of shared autonomygains importance. Shared autonomy refers to the autonomouscontrol of the majority of degrees of freedom in a system,while designing a control interface for human operators tocontrol a reduced number of parameters defining the globalbehavior of the system [78]. For instance, in [79] the au-thors describe the design principles followed in the DARPARobotics Challenge to give the operators of a humanoid robotenough situational awareness while simplifying the actualcontrol of the robot via predefined task sequences.

Another research direction in the control of MRS is human-swarm interaction. Within the EU Guardians project, re-searchers explored the possibilities of human-swarm interac-tion for firefighting, and defined the main design ideas in [80].

In the DARPA SubT challenge, the rules allow only onehuman to communicate with the multi-robot team. A hy-brid autonomous/semi-autonomous model has been proposedin [36], with UAVs being fully autonomous and the largerUGVs could be directly operated in adverse conditions, butare semi-autonomous otherwise.

E. Communication

Communication plays an vital role in an MRS due tothe need of coordination and information sharing necessaryto carry out collaborative tasks. In multi-agent systems, amobile ad-hoc network (MANET) is often formed for wirelesscommunication and routing messages between the robots.Owing to the changing characteristics in terms or wirelesstransmission in different physical mediums, different commu-nication technologies are utilized for various types of robots.An overview of the main MRS communication technologiesis available in [81], while a review on MANET-based com-munication for SAR operations is available in [82].

Collaborative MRS need to be able to communicate tokeep coordinated, but also need to be aware of each other’sposition in order to make the most out of the shared data [83],[84]. Situated communication refers to wireless communica-tion technologies that enable simultaneous data transfer whilelocating the data source [85]. Ubiquitous wireless technologiessuch as WiFi and Bluetooth have been exploited to enablelocalization [86]–[92]. These approaches have been tradition-ally based on the received signal strength indicator (RSSI)and the utilization of either Bluetooth beacons in knownlocations [89]–[91], or radio maps that define the strengthof the signal of different access points over a predefinedand surveyed area [86], [88]. More recently, other approachesrely on angle-of-arrival [87], now built-in in Bluetooth 5.1devices [93]. Ultra-wideband (UWB) technology has emerged

as a more accurate and less prone to interference alternativeto Wi-Fi and Bluetooth [94]. With most existing researchrelying on fixed UWB transceivers in known locations [95],recent works also show promising results in mobile positioningsystems or collaborative localization [96]. A recent trend hasalso been to apply deep learning in positioning estimation [97].

From the point of view of multi-robot coordination, main-taining connectivity between the different agents participatingin a SAR mission is critical. Connectivity maintenance inwireless sensor networks has been a topic of study for the pasttwo decades [98]. In recent years, it has gained more attentionin the fields of MRS with decentralized approaches [99].Connectivity maintenance algorithms can be designed coupledwith distributed control in multi-robot systems [100], or col-lision avoidance [101]. Xiao et al. have recently presenteda cooperative multi-agent search algorithm with connectiv-ity maintenance [102]. Similar works aiming at cooperativesearch, surveillance or tracking with multi-robot systems focuson optimizing the data paths [103] or fallible robots [104],[105]. Another recent work in area coverage with connectivitymaintenance is available in [106]. A comparison of local andglobal methods for connectivity maintenance of multi-robotnetworks from Khateri et al. is available in [107].

In environments with limited connectivity, building andmaintaining communication maps with information about thecoverage and reliability of communication in different areasbrings evident benefits. To this end, Amigomi et al. havepresented a method for updating communication maps inan online manner under connectivity constraints [108]. Asurvey on multi-robot exploration of communication-restrictedenvironments is available in [109].

F. Localization and Deployment in GNSS-Denied Environ-ments

Localization is one of the main challenges in the deploymentof mobile robots. Localization approaches can be dividedamong those providing global localization, and others focusingon relative localization (odometry) with respect to the initialposition during deployment. The former case is most notablyrepresented by GNSS sensors. However, SAR operations canalso occur in GNSS-denied environments (e.g., underground,indoor fires) or environments where GNSS sensors do notprovide enough accuracy (e.g., dense urban environmentsor forests). Global localization with other onboard sensorscan be achieved with image matching [110], or lidar datamatching [111].

Among the different approaches to onboard odometry, vi-sual methods have gained significant traction due to their lowprice, passive nature and flexibility [112]. This is the case,for instance, of visual-inertial odometry with either monocularcameras [113], or multiple sensors [114]. However, these sen-sors present limitations in challenging environments with low-light or low-visibility conditions. In dense urban environments,lidar-based odometry is the only viable solution for long-termautonomy if high-accuracy localization is required [115].

Simultaneous Localization and Mapping (SLAM)approaches utilize odometry algorithms to build local

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maps [116], [117], while utilizing those maps later on formore stable and global localization, where now the globalterm refers to the scope of the mission since deployment, orsince the process of building the map started. The differentteams participating in the DARPA SubT challenge haveemployed various SLAM approaches with both lidar-basedand vision-based approaches. Some of the specific algorithmshave been ORB-SLAM in [39], or Hector SLAM [36].

IV. MULTI-ROBOT COORDINATION

In this section, we describe the main algorithms required formulti-robot coordination and planning in collaborative appli-cations. These are key enablers of MRS capabilities in termsof exploration and navigation over large areas. We discussthis mainly from the point of view of cooperative multi-robotsystems, while focusing on their applicability towards SARmissions. The main problems discussed in this section are thefollowing:- Multi-robot task allocation: distribution of tasks and objec-tives among the robots (e.g., areas to be searched, or positionsto be occupied to ensure connectivity among the robots andwith the base station)- Path planning and area coverage: global path planningcovers area coverage (generation of paths to entirely analyze agiven area) and area partition (dividing the area between mul-tiple robots). Local planning and deals mainly with obstacleand collision avoidance, incorporating robot dynamics.- Area exploration: coverage and mapping algorithms (ordiscover/ search for specific objects) in potentially unknownenvironments.- Centralized multi-robot planning: decision-making on theactions of multiple robots by either gathering and processingdata in a single node, from which decisions are distributedto others, or by achieving consensus through communication(often requiring agents to be aware of all others, and stablecommunication).- Distributed multi-robot planning: algorithms enabling agentsto make independent decisions individually or in subsets basedonly on their own data or data shared by their neighbors. Thesedo not necessarily need agents to be aware of the existence orstate of all other agents in the system.

A. Multi-Robot Task Allocation

Search and rescue operations with multi-robot systemsinvolve aspects including collaborative mapping and situ-ational assessment [118], distributed and cooperative areacoverage [119], or cooperative search [120]. These or othercooperative tasks involve the distribution of tasks and objec-tives within the MRS. In a significant part of the existingmulti-robot SAR literature, this is predefined or done in acentralized manner [6], [16], [18], [28]. Here, we discussinstead distributed multi-robot task allocation algorithms thatcan be applied to SAR operations. Distributed algorithmshave the general advantage of being more robust in adverseenvironments against the loss of individual agents or when thecommunication with the base station is unstable.

A comparative study on task allocation algorithms for multi-robot exploration was carried out by Faigl et al. in [121], con-sidering five distinct strategies: greedy assignment, iterative as-signment, Hungarian assignment, multiple traveling salesmanassignment, and MinPos. However, most of these approachesare often centralized from the decision-making point of view,even if they are implemented in a distributed manner. Others,such as MinPos, shift between the two modalities dependingon the availability of communication. Successive works havebeen presenting more decentralized methods. Decentralizedtask allocation algorithms for autonomous robots are veryoften based on market-based approaches and auction mech-anisms to achieve consensus among the agents [122]–[125].Both of this approaches have been extensively studied for thepast two decades within the multi-robot and multi-agent sys-tems communities [126], [127]. Bio-inspired algorithms havealso been widely studied within the multi-robot and swarmrobotics domains. For instance, in [128], Kurdi et al. present atask allocation algorithm for multi-UAV SAR systems inspiredby locust insects. Active perception techniques have also beenincorporated in multi-robot planning algorithms in existingworks [129], [130]

An early work in multi-robot task allocation for SARmissions was presented by Hussein et al. [122], with a market-based approach formulated as a multiple traveling salesmanproblem. The authors applied their algorithm to real robotswith simulated victim locations that the robots had to divideamong themselves and visit. The solution was optimal (fromthe point of view of distance traveled by the robots) andpath planning for each of the robots was also taken intoaccount. The authors, however, did not study the potential forscalability with the number of robots or victim locations, orconsider the computational complexity of the algorithm. Inthat sense, and with the aim of optimizing the computationalcost owing to the non-polynomial complexity nature of optimaltask allocation mechanisms, Zhao et al. presented a heuristicapproach [123]. The authors introduced a significance measurefor each of the tasks, and utilized both victim locations andterrain information as optimization parameters within theirproposed methodology. The algorithm was tested under asimulation environment with a variable number of rescuerobots and number of survivor locations to test the scalabilityand optimality under different conditions.

An auction-based approach aimed at optimizing a coopera-tive rescue plan within multi-robot SAR systems was proposedby Tang et al. [124]. In this work, the emphasis was also puton the design of a lightweight algorithm more appropriate forad-hoc deployment in SAR scenarios.

A different approach where a human supervisor was con-sidered appears in [131]. Liu et al. presented in this worka methodology for task allocation in heterogeneous multi-robot systems supporting USAR missions. By relying on asupervised system, the authors show better adaptability tosituations with robot failures. The algorithm was tested undera simulation environment where multiple semi-autonomousrobots were controlled by a single human operator.

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(a) Voronoi regions (b) Exact cells

(c) Area triangulation (d) Disjoint area coverage

Fig. 4: Illustration of different basic area decompositionand coverage algorithms: (i) decomposition throughvoronoi regions, (ii) exact cell decomposition, (iii)polygonal decomposition (triangular in this case), and(iv) disjoint area coverage. The resulting decomposi-tions or coverage paths are marked with black lines,while the original areas are shown in gray colors.

B. Area Coverage and Path Planning

An essential part of autonomous SAR operations is pathplanning and area coverage. To this end, multiple algorithmshave been presented for different types of robots or scenarios.

Planning in SAR scenarios can pose additional challengesto well-established planning strategies for autonomous robots.In particular, the locations of victims trapped under debrisor inside cave-like structures might be relatively easy todetermine but significantly complex to access, thus requiringspecific planning strategies. In [120], Suarez et al. presenta survey of animal foraging strategies applied to rescuerobotics. The main methods that are discussed are directedsearch (search space division with memory- and sensory-based search) and persistent search (with either predefinedtime limits or constraint-optimization for deciding how longto persist on the search). With specialized robots being usedfor different scenarios (e.g., tracked robots or crawling robots),the ability of these robots to traverse different environmentsmight not be known a priori. To address this issue, ML-basedtechniques that rely on online learning have been utilized tocreate cost maps of the environment in terms of the ease ofmovement. In [132], the authors introduce a method for a fullyautonomous hexapod walking robot tested on a laboratorytrack with uneven terrain.

Path planning algorithms can be part of area coveragealgorithms or implemented separately for robots to cover theirassigned areas individually. In any case, when area coveragealgorithms consider path planning, it is often from a global

point of view, leaving the local planning to the individualagents. A detailed description of path planning algorithmsincluding approaches of linear programming, control theory,multi-objective optimization models, probabilistic models, andmeta-heuristic models for different types of UAVs is availablein [133]. While some of these algorithms are generic and onlytake into account the origin and objective position, togetherwith obstacle positions, others also consider the dynamics ofthe vehicles and constraints that these naturally impose in localcurvatures, such as Dubin curves [133].

Area coverage and path planning algorithms take into ac-count mainly the shape of the objective area to be surveyed.Nonetheless, a number of other variables are also considered inmore complex algorithms, such as energy consumption, rangeof communication and bandwidth, environmental conditions,or the probability of failure. This data is not necessarily avail-able a priori, and therefore it is also in the interest of the robotsto collect data affecting the planning outcome while operating.The problem of maximizing the utility of data collection iscalled the informative path planning (IPP) problem [134]. IPPapproaches have been shown to outperform more traditionalplanning algorithms such as greedy algorithms and geneticalgorithms [135].

The specific dynamics and capabilities of the robots beingused can also be utilized to optimize the performance of thearea coverage, for example when comparing the maneuver-ability of quadrotors and fixed-wing UAVs. Cabreira et al.have presented algorithms for coverage path planning withUAVs [136].

Area coverage algorithms can be broadly classified in termsof the assumptions they make on the geometry of the area to becovered. The most basic approaches consider only convex andjoint areas [119], for which paths can be efficiently generatedbased on area decomposition algorithms [137], [138]. Some ofthe most common area decomposition and coverage algorithmsare shown in Fig. 4.

Recent works have considered more complex environments.For instance, in [139], Xie et al. presented a path planningalgorithm for UAVs covering disjoint convex regions. Theauthors’ method considered an integration of both coveragepath planning and the traveling salesman problem. In order toaccount for scalability and real-time execution, two approacheswere presented: a near-optimal solution based on dynamicprogramming, and a heuristic approach able to efficientlygenerate high-quality paths, both tested under simulation envi-ronments. Also aiming at disjoint but convex areas, Vazquez etal. proposed a similar method that separates the optimizationof the order in which the different areas were visited and thepath generation for each of them [140]. Both of this cases,however, provide solutions for individual UAVs.

Furthermore, the optimization problems upon which multi-robot area coverage algorithms build are known to belongto the NP-hard class of non-deterministic polynomial timealgorithms [141]. Therefore, part of the existing research hasfocused towards probabilistic approaches. This naturally fitsto SAR operations since, after an initial assessment of theenvironment, SAR personnel can get an a priori idea ofthe most probable locations for victims [142]. The idea of

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using probability distributions in the multi-objective searchoptimization problem has also been extended towards activelyupdating these distributions as new sensor data becomesavailable [143].

C. Planning for different robots: UAVs, UGVs, UUVs andUSVs

Mobile robots operating on different mediums necessarilyhave different constraints and a variable number of degreesof freedom. For local path planning, a key aspect to considerwhen designing control systems is the holonomic nature ofthe robot. In a holonomic robot, the number of controllabledegrees of freedom is equal to the number of degrees offreedom defining the robot’s state. In practice, most robotsare non-holonomic, with some having significant limitationsto their local motion such as fixed-wing UAVs [144], orUSVs [145]. However, quadrotor UAVs, which have gainedconsiderable momentum owing to their flexibility and rel-atively simple control, can be considered holonomic [146].Ground robots equipped with omniwheel mechanisms and ableof omnidirectional motion can be also considered holonomicif they operate on favorable surfaces [147].

Multiple works have been devoted to reviewing the differentpath planning strategies for unmanned vehicles in differentmediums: aerial robots [133], surface robots [148], underwaterrobots [149], [150], and ground robots for urban [55], orwilderness [151] environments. From these works, we havesummarized the main constraints to be considered in pathplanning algorithms in Fig. 5.

The main limitations in robot navigation, and therefore pathplanning, in different mediums can be roughly characterizedby: (i) dynamic environments and movement limitations inground robots; (ii) energy efficiency, situational awareness, andweather conditions in aerial robots; (iii) underactuation andenvironmental effects in surface robots, with currents, windsand water depth constraints; and (iv) localization and commu-nication in underwater robots. Furthermore, these constraintsincrease significantly in SAR operations, with earthquakesaggravating the movement limitations of UGVs, or fires andsmoke preventing normal operation of UAVs. Some emergencyscenarios, such as flooded coastal areas, combine multiple ofthe above mediums making the deployment of autonomousrobots even more challenging. For instance, in [152], theauthors describe path planning techniques for rescue vessels inflooded urban environments, where many of the limitations ofurban navigation are added to the already limited navigationof surface robots in shallow waters.

A key parameter to take into account in autonomous robots,and particularly in UAVs, is energy consumption. Di Franco etal. presented an algorithm for energy-aware path planning withUAVs [153]. A more recent work considering energy-awarepath planning for area coverage introduces a novel algorithmfor path planning that minimizes turns [154]. Energy efficiencyis a topic that has also been considered in USVs. In [155], theauthors introduced an energy-efficient 3D (two-dimensionalpositioning and one-dimension for orientation) path planningalgorithm that would take into account both environmental

effects (marine currents, limited water depth) and the headingor orientation of the vehicle (in the start and end positions).

Owing to the flexibility of quadrotor UAVs, they have beenutilized with different roles in more complex robotic systems.For instance, in [13] the authors describe a heterogeneousmulti-UAV system for earthquake SAR where some of theUAVs are in charge of providing reliable network connection,as a sort of air communication station, while smaller UAVsflying close to the ground are in charge of the actual searchtasks.

D. Multi-Robot Path Planning

Research in the field of multi-robot path planning has beenongoing for over two decades. An early approach to multi-robot cooperation was presented in [156] in 1995, where theauthors introduced an incremental plan-merging approach thatdefined a global plan shared among the robots. In [137], anearly generalization of previous algorithms towards noncon-vex and nonsimply connected areas was presented, enablingdeployment in more realistic scenarios. The advances sincethen have been significant in multiple directions. With theidea or providing fault-tolerant systems, in [119] the authorsintroduced a reconfiguration process that would account inreal-time for malfunctioning or missing agents, and adjust thepaths of remaining agents accordingly. Considering the needof inter-robot communication for aggregating and mergingdata, a cooperative approach to multi-robot exploration thatconsiders the range limitations of the communication systembetween robots was introduced in [157]. Non-polygonal areapartitioning methods have also been proposed. In [158], acircle partitioning method that the authors claim to be appli-cable to real-world SAR operations was presented. Coveringthe topics of connectivity maintenance and IPP, a multi-robotIPP approach to managing continuous connectivity constraintsappears in [159].

Existing approaches often differentiate between area cov-erage and area exploration. In area coverage algorithms, al-gorithms focus on optimally planning paths for traversing aknown area, or dividing a known area among multiple agentsto optimize the time it takes to analyze it. Area explorationalgorithms focus instead on the coverage and mapping ofpotentially unknown environments. The two terms, however,are often used interchangeably in the literature. An overviewand comparison of multi-robot area exploration algorithms isavailable in [160].

In [161], Choi et al. present a solution for multi-UAVsystems, which is in turn focused at disaster relief scenarios.In particular, the authors developed this solution in orderto improve the utilization of UAVs when fighting multiplewildfires simultaneously. Also considering multi-UAV pathplanning, but including non-convex disjoint areas, Wolf et al.proposed a method were the operator could input a desiredoverlap in the search areas [162]. This can be of particularinterest in heterogeneous multi-robot systems where differentrobots have different sensors, and the search personnel wantsmultiple robots to travel over some of the areas. Finally,another recent work in cooperative path planning that focuses

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PlanningConstraints

AerialRobots

SurfaceRobots

UnderwaterRobots

UrbanGroundRobots

Fixed-wing dynamics

Energy efficiency

Altitude limitations

Connectivity maintenance

Ship dynamics

Marine currents

Limited water depths

Limited degrees of freedom

Underwater flows

Water pressure

Real-time communication

Localization in mid-water

Uneven terrain

Underactuated robots

Limited sensing range

Dynamic environment

Fig. 5: Main path planning constraints that autonomous robots in different domains need to account for. Some of these aspectsare common across the different types of robots, such as energy efficiency and inherent constraints from the robots’dynamics, but become more predominant in UAVs and USVs, for instance.

on mountain environments and can be of specific interest inWiSAR operations was presented by Li et al. [163].

A subset of multi-robot path planning algorithms are forma-tion control algorithms. Formation control or pattern formationalgorithms are those that define spatial configurations in multi-robot systems [164]. Most formation control algorithms formulti-agent systems can be roughly classified in three cate-gories from the point of view of the variables that are measuredand actively controlled by each of the agents: position-basedcontrol, displacement-based control, and distance or bearing-based control [164]. Formation control algorithms requiringglobal positioning are often implemented in a centralizedmanner, or through collaborative decision making. Displace-ment and distance or bearing-based control, on the otherhand, enable more distributed implementations with only localinteractions among the different agents [165]–[167]. In SARoperations, formation control algorithms are an integral partof multi-robot ad-hoc networks or MANETs [168], [169],multi-robot emergency surveillance and situational awarenessnetworks [170], or even a source of communication in human-swarm interaction [171].

E. Multi-Objective Multi-Agent Optimization

From a theoretical point of view, a multi-agent collaborativesearch problem can be formulated and solved as a multi-agent and multi-objective optimization problem in a certainspace [172], [173].

In post-disaster scenarios and emergency situations in gen-eral, an initial assessment of the environment often providesrescue personnel an idea of the potential spatial distribution ofvictims [13]. In those cases, different a priori probabilities canbe assigned to different areas, providing a ranking of locationsfor the multi-objective optimization problem. The literatureinvolving multi-agent multi-objective optimization for SARoperations is, however, sparse. In [174], Hayat et al. proposeda genetic algorithm for multi-UAV search in a bounded area.One of the key novelties of this work is that the authorsconsider simultaneously connectivity maintenance among the

UAV network and optimization of area coverage. Moreover,the algorithm could be adjusted to give more priority toeither coverage or connectivity, depending on the missionrequirements. A multi-objective evolutionary algorithm aimedat general emergency response planning was proposed byNarzisi et al. in [175].

F. Planning in Heterogeneous Multi-Robot Systems

Most existing approaches for multi-robot exploration or areacoverage either assume that all agents share similar operationalcapabilities, or that the characteristics of the different agentsare known a priori. Emergency deployments in post-disasterscenarios for SAR of victims, however, requires flexibleand adaptive systems. Therefore, algorithms able to adaptto heterogeneous robots that potentially operate on differentmediums and with different constraints (e.g., UAVs and UGVcollaborating in USAR scenarios) need to be utilized. In thisdirection, Mueke et al. presented a system-level approach fordistributed control of heterogeneous systems with applicationsto SAR scenarios [176]. In general, we see a lack of furtherresearch in this area, as most existing projects and systemsinvolving heterogeneous robots predefine the way in whichthey are meant to cooperate. From a more general perspective,an extensive review on control strategies for collaborative areacoverage in heterogeneous multi-robot systems was recentlypresented by Abbasi [177]. Also from a general perspective, asurvey on cooperative heterogeneous multi-robot systems byRizk et al. is available in [178].

V. SINGLE AND MULTI-AGENT PERCEPTION

In SAR missions, it is essential to be able to quickly detecthumans, hazards, and provide real-time situational awarenessto the robots. In [181], the authors provide a broad overviewof the progress of computer vision covering all sorts ofemergencies. Current state-of-the-art computer vision modelsare based on Deep Learning (DL), which often leads to heavyand slow methods that cannot operate on real-time on portabledevices. However, recent research has also focused towards

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(a) Object detection (b) Image segmentation

Fig. 6: Examples of (a) an image detection algorithm, YOLOv3 [179] with Darknet, which detects a boat with 91% confidence,and (b) a water segmentation output [180].

the development of lighter and faster models able to operatein real-time with limited hardware resources.

This section thus focuses on DL for real-time percep-tion with lightweight models. We review single and multi-agent machine perception methods on SAR-like missions andenvironments, where DL is the key enabler for the actualidentification of victims and assessment of the situation. Ascameras are the most common sensors in SAR robotics, wefirst concentrate on image-based perception, i.e., semanticsegmentation and object detection. In semantic segmentation,everything that the agent perceives is labeled, and in object de-tection, only the objects of interest are labeled. The differenceis illustrated in Fig. 6. We also discuss multi-modal sensorfusion that allows to combine information from cameras andother sensors.

A. Semantic Segmentation

Semantic segmentation is a process, where each pixel inan image is linked to a class label, such as sky, road, orforest. These pixels then form larger areas of adjacent pixelsthat are labeled with the same class label and recognizedas objects. A survey on semantic segmentation using deeplearning techniques available in [182] provides an extensiveview of the methods provided to tackle this problem. In au-tonomous agents in general, the use of semantic segmentationhas been studied fairly well in autonomous road vehicles. Siamet al. [183] have done an in-depth comparison of such semanticsegmentation methods for autonomous driving and proposeda real-time segmentation benchmarking framework.

In marine environment, the study of semantic segmentationhas been less common. In [184], three commonly used state-of-the-art deep learning semantic segmentation methods (U-Net [185], PSP-Net [186] and DeepLabv2 [187]) are bench-marked on a maritime environment. The leaderboard for oneof the largest publicly available datasets, Modd2 [188], alsocontains a listing of semantic segmentation method capable toperform in marine environment [186], [187], [189]–[194].

In our former studies [180], [195], we have focused on se-mantic segmentation to separate water surface from everythingelse that appears in the image, which is similar to the process

that is performed in self-driving cars for road detection. Whileexcellent results can be obtained when the algorithm is appliedin conditions that resemble the training images (see Fig 6b), itwas observed the performance decreases notably in differentconditions. This highlights the need of diverse training imagesand domain adaption techniques that help to adjust to unseenconditions [196].

B. Object Detection

Object detection is a technique related to computer visionand image processing which deals with detecting instancesof semantic objects of a certain class in digital images andvideos. Object detectors can usually be divided into twocategories: two-stage detectors and one-stage detectors. Two-stage detectors first propose candidate object bounding boxes,and then features are extracted from each candidate box forthe following classification and bounding-box regression tasks.The one-stage detectors propose predicted boxes from inputimages directly without region proposal step. Two-stage de-tectors have high localization and object recognition accuracy,while the one-stage detectors achieve high inference speed. Asurvey of deep learning based object detection [197] has beenpublished recently.

Object detection tasks require high computing power andmemory for real-time applications. Therefore, cloud comput-ing [198] or small-sized object detection methods have beenused for UAV applications [199]–[202]. Cloud computingassists the system with high computing power and memory.However, communicating with a cloud server brings unpre-dictable delay from the network. In [198], authors used cloudcomputing for object detection while keeping low-level objectdetection and navigation on the UAV.

Another option is to rely on specific object detection mod-els [199]–[202], designed for limited computational power andmemory. The papers proposed new object detection models,by using old detection models as their base structure andscaling the original network by reducing the number of filtersor changing the layers and they achieved comparable detectionaccuracy besides the speed on real-time applications on drones.In [201], authors observed a slight decrease on the accuracy

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while the new network was faster comparing to the old struc-ture. In [203], an adaptive submodularity and deep learning-based spatial search method for detecting humans with UAVin a 3D environment was proposed.

C. Fast and computationally light methods

As mentioned before, some solutions can be rather slowand computationally heavy, but in SAR operations it is vitalthat the used algorithms are as real-time as possible while stillworking with high level of confidence. The faster the algorithmcan work, the faster the agent can search the area and thatprobably could lead to faster rescue of the persons in distress.Also the high confidence assures that no important informationis missed.

You only look once (YOLO) is the state-of-the-art, real-time object detection system, and YOLOv3 [179] is statedto be extremely fast and accurate compared to methods likeR-CNN [204] and Fast R-CNN [205]. An example of theYOLOv3 output is shown in Fig. 6a.

There is active research on methods that can produce morecompact networks with improved prediction capability. Com-mon approaches include knowledge distillation [206], where acompact student network is trained to mimic a larger network,e.g., by guiding the network to produce similar activationsfor similar inputs, and advanced network models, such asOperational Neural Networks [207], where the linear operatorsof CNNs are replaced by various (non-)linear operations,which allows to produce complex outputs which much fewerparameters.

D. Multi-Modal Information Fusion

Multi-modal information fusion aims at combining datafrom a multiple sources, e.g., images and LiDAR. Infor-mation fusion techniques have been actively researched fordecades and there is a myriad of different ways to approachthe problem. The approaches can be roughly divided intotechniques fusing information on raw data/input level, onfeature/intermediate level, or on decision/output level [210].An overview of the main data fusion approaches in multi-modal scenarios is illustrated in Fig. 7.

Some of the main challenges include representation, i.e.,how to represent multi-modal data taking into account comple-mentarity and redundancy of multiple modalities, translation,i.e., how to map the data from different modalities to a jointspace, alignment, i.e., how to understand the relations of theelements of data from different modalities, for example, whichparts of the data describe the same object in an image and in apoint-cloud produced by LiDAR, fusion, i.e., how to combinethe information to form a prediction, and co-learning, i.e., howto transfer knowledge between the modalities, which may beneeded, for example, when one the modalities is not properlyannotated [211]. The main challenges related to multi-modaldata are listed in Table IV.

In research years, also the information fusion techniqueshave focused more and more on big data and deep learning.Typical deep learning data fusion techniques have some layersspecific to each data source and the features can be then

combined before the final layers or processed separately allthe way to the network output, while the representationsare coordinated through a constraint such as a similaritydistance [211], [212].

In SAR operations, the most relevant data fusion applica-tions concern images and depth information [213], [214]. Arecent deep learning based approach uses the initial image-based object detection results are to extract the correspondingdepth information [214] and, thus, fuses the modalities onthe output level. Another recent work proposed a multi-scalemulti-path fusion network with that follows a two-streamfusion architecture with cross-modal interactions in multiplelayers for coordinated representations [213]. Simultaneouslocalization and mapping (SLAM) aims at constructing orupdating a map of the environment of an agent, while simul-taneously keeping track of the agent’s position. In SLAM,RGB-D data is used to build a dense 3D map and the datafusion technique applied in a single-agent SLAM is typicallyextended Kalman Filter (EKF) [215]. Fusing RGB and thermalimage data can be needed, for example, in man overboardsituations [216]. Typically, there is much less training dataavailable for thermal images and, therefore, domain adaptionbetween RGB and thermal images may help [217].

E. Multi-Agent Perception

To get the full benefit of the multi-robot approach in SARoperations, there should be also information fusion betweenthe agents. For example, an object seen from two differentangles can be recognized with a higher accuracy. The sensorscarried by different robots may be the same, typically cameras,or different as the presence of multiple agents makes itpossible to distribute some of the sensors’ weight between theagents, which is important especially in UAV applications. Thegoal is that the perception the agents have of their environmentis based on aggregating information from multiple sources andthe agents share information steadily between themselves orto a control station.

The challenges and approaches are similar to those dis-cussed in Section V-D for multi-modal information fusion, butthe situation is further complicated by the fact that the datato be fused is located in different physical locations and thesensors are now moving with respect to each other. Some ofthe challenges that need to be solved are where to perform datafusion, how to evaluate whether different agents are observingthe same objects or not, or how to rank observations fromdifferent agents. For many of the challenges, there are noefficient solutions yet.

There are several works concentrating on target trackingby multiple agents. These can be divided into four maincategories: 1) Cooperative Tracking (CT), which aims trackingmoving objects, 2) cooperative multi-robot observation ofmultiple moving targets (CMOMMTs), where the goal isto increase the total time of observation for all targets, 3)cooperative search, acquisition, and tracking (CSAT), whichalternates between the searching and tracking of moving tar-gets, and 4) multi-robot pursuit evasion (MPE) [218], [219]. InSAR operations, especially CSAT approaches can be important

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Sensor 1

Sensor 2

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association

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and fusion

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(c) True fusion using features (high-level features or multivariate features).

Sensor 1

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Sensor N

Raw /signal-level

datafusion

Combinedfeature

extraction

High-leveldecisionmaking

FusedResult

(d) True fusion with minimal reduction.

Fig. 7: Different multi-modal data fusion approaches: (a) parallel data integration with high-level decision making, (b) sequentialprocessing of modalities when different modalities have difference conficende or quality levels, (c) true fusion withhigh-level features or with multivariate features, and (d) true fusion with minimal reduction [208], [209]. In gray, wehighlight the stage in which the fusion happens.

TABLE IV: Main challenges in multi-modal and multi-source data fusion

Challenge Description

Noisy data Different data sources suffer from different types and magnitudes of noise. A heterogeneous set of data sources naturally comeswith heterogeneous sources of noise, from calibration errors to thermal noise.

Unbalanced data Having different data sources often involves data with different characteristics in terms of quality and confidence, but also interms of spatial and temporal resolution.

Conflicting data Data from different sources might yield conflicting features. For example, in the case of autonomous robots, different types ofsensors (visual sensors, laser rangefinders or radars) might detect obstacles at different distances. Missing data over a certaintime interval from one of the sources might also affect the data fusion.

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after the victims have been initially located, for example, inmarine SAR operations, where the victims are floating in thewater. For initial search of the victims, a simulated cooperativeapproach using scanning laser range finders was proposedin [220], but multi-view image fusion techniques for SARoperations are not yet operational.

VI. CLOSING THE LOOP:ACTIVE PERCEPTION IN MULTI-ROBOT SYSTEMS

While we above discussed coverage planning, formationcontrol, and perception aspects of SAR as separate opera-tions, it is obvious that all the components need to functionseamlessly together in order achieve optimal performance.This means that coverage planning and formation control needto be adjusted based on the observations and the perceptionalgorithms need to be optimized to support and take fulladvantage of overall adaptive multi-agent systems. This can beachieved via active perception techniques [221], [222]. Whilethe passive perception techniques simply utilize whateverinputs they are given, active perception methods adapt thebehavior of the agent(s) in order to obtain better inputs.

Active perception has been defined as:

An agent is an active perceiver if it knows why itwishes to sense, and then chooses what to perceive,and determines how, when, and where to achieve thatperception. [223]

In the case of searching a victim, this can mean that the robotsare aware that the main purpose is to save humans (why), andare able adapt their actions to achieve better sightings of peoplein need of help (what) by, for example, zooming the camerato a potential observation (how) or by moving to a positionthat allows a better view (where and when).

In a SAR operation, active perception can help in multiplesubtasks in the search for victims, such as path finding incomplex environments [224], obstacle avoidance [225], ortarget detection [226]. Once a victim has been detected, itis also important to keep following him/her. For instance, inmaritime SAR operations, there is a high probability that thesurvivors are floating in the sea and drifting due to the windor marine currents. In such scenarios, it is essential that therobots are able to continuously update the position of survivorsso that path planning for the rescue vessel can be re-optimizedand recalculated in real-time in an autonomous manner. Thisrequires active tracking of the target [227].

While our main interest lies in active perception for multi-robot SAR operations, the literature directly focusing on thisspecific field is still scarce. Nevertheless, active perception is arapidly developing research topic and we believe that it will beone of the key elements also in the future research on multi-robot SAR operations. Therefore, we start by introducing themain ideas presented in single-agent active perception and thenturn our attention on works that consider active perception information control and multi-robot planning. The essence ofactive perception is understanding, adapting to changes in theenvironment and taking action for the next mission step.

A. Single-Agent Active Perception

Besides performing their main task (e.g., object detection),active perception algorithms use the same input data to predictthe the next action that can help them to improve theirperformance. This is a challenge for training data collection,because typically there is high number of possible actionsin any given situation and it is not always straightforwardto decide which actions would be good or bad. A bench-mark dataset [228] provides 9000 real indoor input imagesalong with the information showing what would be seen nextif a specific action is carried out when a specific imageis seen. Another possibility is to create simulated trainingenvironments [229], where actions can be taken in a morenatural manner. With such simulators, it is critical that thesimulator is realistic enough so that employment in the realworld is possible. To facilitate the transition, Sim2Real learn-ing methods can be used [230]. Finally, it is also possibleto use real equipment and environments [224], [231], butsuch training is slow and requires having access to suitableequipment. Therefore, training setups are typically simplistic.Furthermore, real-world training makes it more complicatedto compare different approaches.

Currently, the most active research direction in active per-ception is reinforcement learning [222]. Instead of learningfrom labeled input-output pairs, reinforcement learning isbased on rewards and punishment given to the agents basedon their actions. While reinforcement learning is expected tobe the future direction is active perception, its applicabilityin SAR operations is reduced by the problems of collectingor creating sufficient training data and experiences. Therefore,simpler approaches that use deep neural networks only forvisual data analysis but use traditional approaches, such asproportional-integral-derivative (PID) controllers [232], forcontrol may be currently easier to implement. A way to useactive perception in a simulated setting of searching a lostchild indoors using a single UAV is described in [226].

B. Perception Feedback in Multi-Robot Planning and Multi-Robot Search

Other works in cooperative active tracking and cooperativeactive localization, have been presented without necessarilyconsidering spatial coordination of fixed formations amongthe collaborative robots. In [233], active perception was in-corporated in a collaborative multi-robot tracking applicationby planning the paths to minimize the uncertainty in thelocation of both each individual robot and the target. Therobots were UAVs equipped with lidar sensors. In [234], theauthors extend the previous work towards incorporating thedynamics of the UAVs in the position estimators with performreal-world experiments. In this second work, a hierarchicalcontrol approach was utilized to generate the paths for thedifferent robots.

An extensive description of methods for (i) localizationof a stationary target with one and many robots, (ii) activelocalization of clusters of targets, (iii) guaranteed localizationof multiple targets, and (iv) tracking adversarial targets, ispresented in [235]. The different methods incorporate both

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active perception and active localization approaches, and theyare mainly focused at ranging measurements based on wirelesssignals. In terms of SAR robotics and the different systemsdescribed in this survey, these type of methods have the mostpotential in avalanche events for locating ATs, or in otherscenarios if the victims have known devices emitting somesort of wireless signal.

In the area of multi-robot search, Acevedo et al. recentlypresented a cooperative multi-robot search algorithm basedon a particle filter and active perception [236]. The approachpresented in that paper can be exported to SAR scenarios,as the authors focus on optimizing the collaborative searchby actively maximizing the information that robots acquireof the search area. One of the most significant contributionswithin the scope of this survey is that the authors workon the assumption of uncertainty in the data, and thereforepropose the particle filter for active collaborative perception.This results in a dynamic reallocation of the robots to differentsearch areas. The system, while mostly distributed, requires therobots to communicate with each other to maintain a commoncopy of the particle filter. The authors claim that future workswill be directed towards further decentralizing the algorithmsby enabling asynchronous communication and local particlefilters at each of the robots.

In between the areas of multi-robot active coverage andactive tracking and localization, Tokekat and Vander et al.have presented methods for localizing and monitoring radio-tagged invasive fish with an autonomous USV [237], [238].Other authors have presented methods for actively acquiringinformation about the environment. For instance, a significantwork in this area that has direct application to the initialassessment and posterior monitoring of the area in SARscenarios is [239], where the authors present a decentralizedmulti-robot simultaneous localization and mapping (SLAM)algorithm. The authors identified that optimal path planningalgorithms maximizing active perception had a computationalcomplexity that would grow exponentially with both the num-ber of sensors and the planning horizon. To address this issue,they proposed an approximation algorithm and a decentralizedimplementation with only linear complexity demonstrated witha multi-robot SLAM experiment.

A work on the combination of cooperative tracking togetherwith formation control algorithms for multi-robot systemswas introduced in [240]. The authors proposed a perception-driven formation control algorithms that aimed at maximizingthe performance of multi-robot collaborative perception of atracked subject through a non-linear model predictive control(MPC) strategy.

In a similar research direction, Tallamraju et al. described ina recent work a formation control algorithm for active multi-UAV tracking based on MPC [241]. One of the main noveltiesof this work is that the MPC is built from decoupling theminimization of the tracking error (distance from the UAVsto the person) and the minimization of the formation error(constraints on the relative bearing of the UAVs with respectto the tracked person). Another key novelty is that the authorsincorporated collision avoidance within the main control loop,avoiding non-convexity in the optimization problem by calcu-

lating first the collision avoidance constraints and adding themas control inputs to the MPC formulation.

In more practical terms, the results of [241] enable onlinecalculation of collision-free path planning while tracking amovable subject and maintaining a certain formation configu-ration around the tracked subject, optimizing the estimationof the object’s position during tracking and maintaining itclose to the center of the field of view of each of the robotsdeployed for collaborative tracking. Compared to other recentworks, the authors are able to obtain the best accuracy inthe estimation of the tracked person’s position, while onlytrading off a negligible increase in error of the self-localizationestimation of each of the tracking robots.

A more general approach to collaborative active sensingwas presented in [242], where the authors proposed a methodfor planning multi-robot trajectories. This approach could beapplied to different tasks including active mapping with bothstatic and dynamic targets, or for mapping environments withobstacles.

VII. DISCUSSION AND OPEN RESEARCH QUESTIONS

Research efforts have mainly focused on the design ofindividual robots autonomously operating in emergency sce-narios, such as those presented in the European RoboticsLeague Emergency Tournament. Most of the existing literaturein multi-robot systems for SAR either relies on an externalcontrol center for route planning and monitoring, on a staticbase station and predefined patterns for finding objectives, orhave predefined interactions between different robotic units.Therefore, there is a big potential to be unlocked throughouta wider adoption of distributed multi-robot systems. Keyadvances will require embedding more intelligence in therobots with lightweight deep learning perception models, thedesign and development of novel distributed control tech-niques, and a closer integration of perception and controlalgorithms. Moreover, heterogeneous multi-robot systems haveshown significant benefits when compared to homogeneoussystems. In that area, nonetheless, further research needs tofocus on interoperability and ad-hoc deployments of multi-robot systems.

Based on the different aspects of multi-robot SAR thathave been described in this survey, both at the system leveland from the coordination and perception perspectives, wehave summarized the main research directions where we seethe greatest potential. Further development in these areas isrequired to advance towards a wider adoption of multi-robotSAR systems.

A. Shared Autonomy

With the increasing adoption of multi-robot systems forSAR operations over individual and complex robots, thenumber of degrees of freedom that can be controlled hasrisen dramatically. To enable efficient SAR support from thesesystems without the need for a large number of SAR personnelcontrolling or supervising the robots, the concept of sharedautonomy needs to be further explored.

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The applications of more efficient shared autonomy andcontrol interfaces are multiple. For instance, groups of UAVsflying in different formation configurations could provide real-time imagery and other sensor information from a large areaafter merging the data from all the units. In that scenario, theSAR personnel controlling the multi-UAV system would onlyneed to specify the formation configuration and control thewhole system as a single UAV would be controlled in a moretraditional setting.

While some of the directions towards designing controlinterfaces for scalable homogeneous multi-robot systems arerelatively clear, further research needs to be carried outin terms of conceptualization and design of interfaces forcontrolling heterogeneous robots. These include land-air sys-tems (UGV+UAV), sea-land systems (USV+UAV), and alsosurface-underwater systems (USV+UUV), among other pos-sibilities. In these cases, owing to the variability of theiroperational capabilities and significant differences in the robotsdynamics and degrees of freedom, a shared autonomy strategyis not straightforward.

B. Operational EnvironmentsSome of the main open research questions and opportunities

that we see for each of the scenarios described in this paperin terms of deployment of multi-robot SAR systems are thefollowing:

• Urban SAR: we have described the various types ofground robots being utilized in USAR scenarios andcollaborative UGV+UAV systems. In this area, we seethe main opportunities and open challenges to be in (i)collaborative localization in GNSS denied environments;(ii) collaborative perception of victims from differentperspectives; (iii) ability to perform remote triage andestablish a communication link between SAR personneland victims, or to transport medicines and food; and (iv)more scalable heterogeneous systems with various sizesof robots (both UGVs and UAVs) capable to collabo-ratively mapping and monitoring harsh environments orpost-disaster scenarios.

• Marine SAR: throughout this survey, we have seenthat marine SAR operations are one of the scenarioswhere heterogeneous multi-robot systems have been mostwidely adopted. Nonetheless, there are multiple chal-lenges remaining in terms of interoperability and deploya-bility. In particular, few works have explored the potentialin closely designing perception and control strategies forcollaborative multi-robot systems including underwater,surface and aerial robots [243]. Moreover, while thedegree of autonomy of UAVs and UUVs has advancedconsiderably in recent years, USVs can benefit from thedata gathered by these to increase their autonomy. Interms of deployability, more robust solutions are neededfor autonomous take-off and docking of UAVs or UUVsfrom surface robots. Finally, owing to the large areas inwhich search for victims takes place in maritime SARoperations, active perception approaches increasing theefficiency of search tasks have the most potential in theseenvironments.

• Wilderness SAR: some of the most important challengesin WiSAR operations are the potentially remote andunexplored environments posing challenges to both com-munication and perception. Therefore, an essential steptowards more efficient multi-robot operations in WiSARscenarios is to increase the level of autonomy and theoperational time of the robots. Long-term autonomy andembedded intelligence on the robots for decision-makingwithout human supervision are some of the key researchdirections in this area in terms of multi-robot systems.

C. Sim-to-real Methods for Deep Learning

Deep-learning-based methods are flexible and can beadapted to a wide variety of applications and scenarios.Good performance, however, comes at the cost of enoughtraining data and an efficient training process that is carriedout offline. Other deep learning methods, and particularlydeep reinforcement learning (DRL), rely heavily on simulationenvironments for converging towards working control policiesor stable inference, with training happening on a trial-and-errorbasis. Search and rescue robots are meant to be deployed inreal scenarios where the conditions can be more challengingthan those of more traditional robots. Therefore, an importantaspect to take into account is the transferability of the modelstrained in simulation to the reality.

Recent years have seen an increasing research interest inclosing the gap between simulation and reality in DRL [244].In the field of SAR robotics, a relevant example of theutilization of both DL and DRL techniques was presentedby Sampedro et al. [245]. The authors developed a fullyautonomous aerial robot for USAR operations in which aCNN was trained to for target-background segmentation, whilereinforcement learning was utilized for vision-based controlmethods. Most of the training happened with a Gazebo sim-ulation and ROS, and the method was tested also in realindoor cluttered environments. In general, and compared withother DL methods, DRL has the advantage in that it can beused to provide an end-to-end model from sensing to actua-tion, therefore integrating the perception and control aspectswithin a single model. Other recent applications of DRL forSAR robotics include the work of Niroui et al. [246], withan approach to navigation in complex and unknown USARcluttered environments that used DRL for frontier exploration.In this case, the authors put an emphasis on the efficiency ofthe simulation-to-reality transfer. Another recent work by Liet al. [247] showed the versatility of DRL for autonomousexploration and the ability of transferring the model fromsimulation to reality in unknown environments. We discuss therole of DRL in active perception in Section VI. Bridging thegap between simulation and reality is thus another challengein some of the current SAR robotic systems.

D. Human Condition Awareness and Triage

As we have discussed in multiple occasions throughout thissurvey, the current applicability of SAR robotics is mainly inthe search of victims or the assessment and monitoring of thearea by autonomously mapping and analyzing the accident or

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disaster scenario. However, only a relatively small amount ofworks in multi-robot SAR robotics have been paying attentionto the development of methods for increasing the awarenessof the status of the victims in the area or performing remotetriage.

The potential for lifesaving applications in this area issignificant. The design and development of methods for robotsto be able to better understand the conditions of survivors afteran accident is therefore a research topic with multiple openquestions and challenges. Nonetheless, it is important to takeinto account that this most likely requires the robots to reachto the victims or navigate near them. The control of the robotand its awareness of its localization and environment thus needto be very accurate, as otherwise operating in such safety-critical scenario might be counterproductive. Therefore, beforebeing able to deploy in a real scenario novel techniques forhuman condition awareness and remote triage, the robustnessof navigation and localization methods in such environmentsneeds to be significantly streamlined.

E. Heterogeneous Multi-Robot Systems

Across the different types of SAR missions that have beendiscussed in this survey, the literature regarding the utilizationof heterogeneous robots has shown the clear benefits ofcombining either different types of sensors, different perspec-tives, or different computational or operational capabilities.Nonetheless, most of the existing literature assumes that theidentity and nature of the robots and the way in which theycommunicate and share data is known a priori. A wider adop-tion and deployment of heterogeneous multi-robot systemstherefore needs research to advance in the following practicalareas:

• Interoperability: flexible deployment of a variable typeand number of robots for SAR missions requires thecollaborative methods to be designed with wider inter-operability in mind. Interoperability has been the focusof both the ICARUS and DARIUS projects [30], [49].Moreover, extensive research has been carried out ininteroperable communication systems, and current roboticmiddlewares, such as ROS2 [248], enable distributedrobotic systems to share data and instructions withstandard data types. Nonetheless, there is still a lackof interoperability in terms of high-level planning andcoordination for specific missions. In SAR robotics, theseinclude collaborative search and collaborative mappingand perception.

• Ad-hoc systems: closely related to the concept of interop-erability in terms of high-level planning, wider adoptionof multi-robot SAR systems requires these systems tobe deployed in an ad-hoc manner, where the type ornumber of robots does not need to be predefined. Thishas been explored, to some extent, in works utilizingonline planning strategies that account for the possibilityof malfunctioning or missing robots [119].

• Situational awareness and awareness of other robots:the wide variety of robots being utilized in SAR mis-sions, and the different scenarios in which they can

be applied, calls for the abstraction and definition ofmodels defining these scenarios but also the way inwhich robots can operate with them. In heterogeneousmulti-robot systems, distributed high-level collaborativeplanning requires robots to understand not only how canthey operate in their current environment and what are themain limitations or constraints, but also those conditionsof different robots operating in the same environment.For instance, a USV collaborating with other USVs andUAVs in a maritime SAR mission needs to be aware ofthe different perspectives that UAVs can bring into thescene, but also of their limitations in terms of operationaltime or weather conditions.

F. Active Perception

We have closed this survey exploring the literature in activeperception for multi-robot systems, where we have seen a clearlack of research within the SAR robotics domain. Currentapproaches for area coverage in SAR missions, for instance,mostly consider an a priori partition of the area among theavailable robots. Dynamic or online area partitioning algo-rithms are only considered either in the presence of obstacles,or when the number of robots changes [119]. Other worksalso consider an a priori estimation of the probability oflocating victims across different areas to optimize the pathplanning [142], [143]. These and other works are all basedin either a priori-knowledge of the area, or otherwise partitionthe search space in a mostly homogeneous manner. Therefore,there is an evident need for more efficient multi-robot searchstrategies

Active perception can be merged into current multi-robotSAR systems in multiple directions: actively updating andestimating the probabilities of victims’ locations, but also withactive SLAM techniques by identifying the most severelyaffected areas in post-disaster scenarios. In wilderness andmaritime search and rescue where tracking of the victimsmight be necessary even after they have been found, activeperception has the potential to significantly decrease the prob-ability of missing a target.

In general, we also see the potential of active percep-tion within the concepts of human-robot and human-swarmcooperation, and in terms of increasing the awareness thatrobots have of victims’ conditions. Regarding human-robotand human-swarm cooperation, active perception can bringimportant advantages in the understanding the actions of SARpersonnel and being able to provide more relevant supportduring the missions.

VIII. CONCLUSION

Among the different civil applications where multi-robotsystems can be deployed, search and rescue (SAR) operationsare one of the fields where the impact can be most significant.In this survey, we have reviewed the status of SAR roboticswith a special focus on multi-robot SAR systems. While SARrobots have been a topic of increasing research attention forover two decades, the design and deployment of multi-robotsystems for real-world SAR missions has only been effective

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more recently. Multiple challenges remain at the system-level(interoperability, design of more robust robots, and deploymentof heterogeneous multi-robot systems, among others), as wellas from the algorithmic point of view of multi-agent controland multi-agent perception. This is the first survey, to the bestof our knowledge, to analyze these two different points ofview complementing the system-level view that other surveyshave given. Moreover, this work differentiates from othersin its discussion of both heterogeneous systems and activeperception techniques that can be applied to multi-robot SARsystems. Finally, we have listed the main open researchquestions in these directions.

ACKNOWLEDGMENT

This research work is supported by the Academy of Fin-land’s AutoSOS project (Grant No. 328755).

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