Examining application-specific resiliency implementations in UAV swarm scenarios

Abhishek Phadke , F. Antonio Medrano

Intelligence & Robotics ›› 2023, Vol. 3 ›› Issue (3) : 453 -78.

PDF
Intelligence & Robotics ›› 2023, Vol. 3 ›› Issue (3) :453 -78. DOI: 10.20517/ir.2023.27
Review

Examining application-specific resiliency implementations in UAV swarm scenarios

Author information +
History +
PDF

Abstract

The number of real-world scenarios where the use of an unmanned aerial vehicle (UAV) swarm is beneficial has greatly increased in recent years. From precision agriculture to forest fire monitoring, post-disaster search and rescue applications, to military use, the applications are widespread. While it is a perceived requirement that all UAV swarms be inherently resilient, in reality, it is often not so. The incorporation of resilient mechanisms depends on an application usage scenario. This study examines a comprehensive range of application scenarios for UAV swarms to bring forward the multitude of components that work together to provide a measure of resilience to the overall swarm. A three-category scheme is used to classify swarm applications. While systemic resilience is an interconnected concept, most real-world applications of UAV swarm research focus on making certain components resilient to disturbances. A broad categorization of UAV swarm applications, categorized by recognized components and modules, is presented, and prevalent approaches for novel resilience mechanisms in each category are discussed.

Keywords

UAV / UAS / drone / resilience / disruptions

Cite this article

Download citation ▾
Abhishek Phadke, F. Antonio Medrano. Examining application-specific resiliency implementations in UAV swarm scenarios. Intelligence & Robotics, 2023, 3(3): 453-78 DOI:10.20517/ir.2023.27

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Wang D,Wu J.In-flight initial alignment for small UAV MEMS-based navigation via adaptive unscented Kalman filtering approach.Aerospace Science and Technology2017;61:73-84

[2]

Paredes WD,Vakilinia I.LoRa technology in flying ad hoc networks: a survey of challenges and open issues.Sensors2023;23:2403. PMCID:PMC10007589

[3]

Davoli L,Ferrari G.Hybrid LoRa-IEEE 802.11s opportunistic mesh networking for flexible UAV swarming.Drones2021;5:26.

[4]

Cheng J,Deng T.A novel ranging and IMU-Based method for relative positioning of two-MAV formation in GNSS-denied environments.Sensors2023;23:4366 PMCID:PMC10181704

[5]

Ortiz-villarejo AJ.A low-cost, easy-way workflow for multi-scale archaeological features detection combining lidar and aerial orthophotography.Remote Sens2021;13:4270

[6]

Friess C,Polonelli T,Benini L.Fully onboard SLAM for Distributed mapping with a swarm of Nano-Drones.arXiv2023;03678

[7]

Woods DD.Four concepts for resilience and the implications for the future of resilience engineering.Reliab Eng Syst Safe2015;141:5-9.

[8]

Madni AM,Sievers M.Constructing models for systems resilience: challenges, concepts, and formal methods.Systems2020;8:3

[9]

Ordoukhanian E.Model-based approach to engineering resilience in multi-UAV systems.Systems2019;7:11

[10]

Phadke A,Chu T.Engineering resiliency in UAV swarms-a bibliographic analysis.J Phys: Conf Ser2022;2330:012007

[11]

Wang J,Li Z,Bian Y.VSAI: A multi-view dataset for vehicle detection in complex scenarios using aerial images.Drones2022;6:161

[12]

VOSviewer-visualizing scientific landscapes. Available from: https://www.vosviewer.com [Last accessed on 21 Sep 2023]

[13]

Iqbal U,Zhao J,Perez P.Drones for flood monitoring, mapping and detection: a bibliometric review.Drones2023;7:32.

[14]

Rodríguez M, Melgar SG, Cordero AS, Márquez JMA. A critical review of unmanned aerial vehicles (UAVs) use in architecture and urbanism: scientometric and bibliometric analysis.Appl Sci2021;11:9966

[15]

Phadke A.Towards resilient UAV swarms-a breakdown of resiliency requirements in UAV swarms.Drones2022;6:340

[16]

Abdelkader M,Jaleel H.Aerial swarms: recent applications and challenges.Curr Robot Rep2021;2:309-20 PMCID:PMC8294305

[17]

Phadke A,Sekharan CN.Designing UAV Swarm experiments: a simulator selection and experiment design process.Sensors2023;23:7359 PMCID:PMC10490248

[18]

Merz M,Skliros V.Autonomous UAS-based agriculture applications: general overview and relevant european case studies.Drones2022;6:128.

[19]

Pearson S,Valluru R.Robotics and Autonomous systems for net zero agriculture.Curr Robot Rep2022;3:57-64

[20]

Lee HS,Thomasson JA,Zhang Z.Development of multiple UAV collaborative driving systems for improving field phenotyping.Sensors2022;22:1423 PMCID:PMC8880027

[21]

Zhang W,Li N,Sun T.Review of current robotic approaches for precision weed management.Curr Robot Rep2022;3:139-51 PMCID:PMC9305686

[22]

Tsouros DC,Sarigiannidis PG.A review on UAV-based applications for precision agriculture.Information2019;10:349

[23]

Odonkor P,Chowdhury S.Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping.Swarm and Evolutionary Computation2019;46:52-68

[24]

Vasilijevic A,Lopez-Castejon F.Heterogeneous robotic system for underwater oil spill survey. Genova, Italy; 2015, pp. 1-7.

[25]

Roldán JJ,Garzón M,Del Cerro J.Heterogeneous multi-robot system for mapping environmental variables of greenhouses.Sensors2016;16:1018 PMCID:PMC4970068

[26]

Goian A,Ahmad U,Almoosa N.Victim localization in USAR scenario exploiting multi-layer mapping structure.Remote Sens2019;11:2704

[27]

Cardona GA.Robot Swarm navigation and victim detection using rendezvous consensus in search and rescue operations.Appl Sci2019;9:1702

[28]

Siemiatkowska B.A framework for planning and execution of drone swarm missions in a hostile environment.Sensors2021;21:4150 PMCID:PMC8234058

[29]

Gans NR.Cooperative multirobot systems for military applications.Curr Robot Rep2021;2:105-11

[30]

Ko Y,Duguma DG,You I.Drone secure communication protocol for future sensitive applications in military zone.Sensors2021;21:2057 PMCID:PMC8000982

[31]

Nex F,Steenbeek A.Towards real-time building damage mapping with low-cost UAV solutions.Remote Sens2019;11:287.

[32]

Zhang R,Duan K.Automatic detection of earthquake-damaged buildings by integrating UAV oblique photography and infrared thermal imaging.Remote Sens2020;12:2621

[33]

Nagasawa R,Moya L.Model-based analysis of multi-UAV path planning for surveying postdisaster building damage.Sci Rep2021;11:18588. PMCID:PMC8452788

[34]

Outay F,Adnan M.Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges.Transp Res Part A Policy Pract2020;141:116-29 PMCID:PMC7527789

[35]

Klaine PV,Souza RD.Distributed drone base station positioning for emergency cellular networks using reinforcement learning.Cognit Comput2018;10:790-804 PMCID:PMC6182572

[36]

Hydher H,Hemachandra KT.Intelligent UAV Deployment for a disaster-resilient wireless network.Sensors2020;20:6140. PMCID:PMC7662562

[37]

Ferrag MA.Deliverycoin: an ids and blockchain-based delivery framework for drone-delivered services.Computers2019;8:58.

[38]

Rinaldi M,Bugaj M,Guglieri G.Development of heuristic approaches for last-mile delivery tsp with a truck and multiple drones.Drones2023;7:407

[39]

Phadke A.Updating the taxonomy of intrusion detection systems. in proceedings of the 2021 IEEE 45th annual computers, software, and applications conference (COMPSAC). Madrid, Spain; 2021.pp.1085-91.

[40]

Miao Y,Alzahrani BA,Alafif T.Airborne LiDAR assisted obstacle recognition and intrusion detection towards unmanned aerial vehicle: architecture, modeling and evaluation.IEEE Trans Intell Transport Syst2021;22:4531-40

[41]

Quamar MM.Control and coordination for swarm of uavs under multi-predator attack. in proceedings of the 2023 systems and information engineering design symposium (SIEDS); 2023 April 96-101; Charlottesville, VA, USA.

[42]

Chi P,Wu K,Wang Y.A bio-inspired decision-making method of UAV swarm for attack-defense confrontation via multi-agent reinforcement learning.Biomimetics2023;8:222 PMCID:PMC10296010

[43]

Opromolla R,Fasano G.Airborne visual detection and tracking of cooperative uavs exploiting deep learning.Sensors2019;19:4332 PMCID:PMC6806143

[44]

Makkapati VR,P .Apollonius allocation algorithm for heterogeneous pursuers to capture multiple evaders.arXiv2006;10253.

[45]

Lappas V,Tsourdos A.Autonomous unmanned heterogeneous vehicles for persistent monitoring.Drones2022;6:94

[46]

Sial MB,Wang S.Bearing-based distributed formation control of unmanned aerial vehicle swarm by quaternion-based attitude synchronization in three-dimensional space.Drones2022;6:227

[47]

Li S,Wang S.Distributed bearing-only formation control for UAV-UWSV heterogeneous system.Drones2023;7:124.

[48]

Vásquez BL, Barca JC. Adversarial scenarios for herding UAVs and counter-swarm techniques.Robotica2023;41:1436-51.

[49]

Jiang B,Li T,Shi M.Robust Cooperative control of UAV swarms for dual-camp divergent tracking of a heterogeneous target.Drones2023;7:306.

[50]

Cai Y,Li R,Yuan J.Joint trajectory and resource allocation design for energy-efficient secure uav communication systems.IEEE Trans Commun2020;68:4536-53

[51]

Zimroz P,Wróblewski A.Application of UAV in search and rescue actions in underground mine-A specific sound detection in noisy acoustic signal.Energies2021;14:3725

[52]

Wang C,Zhang T.The adaptive vortex search algorithm of optimal path planning for forest fire rescue UAV. In Proceedings of the Advanced Information Technology, Electronic and Automation Control Conference(IAEAC 2018). Chongqing, China; 2018.pp.400-03.

[53]

Ausonio E,Ghio M.Drone swarms in fire suppression activities: a conceptual framework.Drones2021;5:17

[54]

Hu J,Carrasco J,Arvin F.Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring.Aerospace Science and Technology2022;123:107494

[55]

Saffre F,Karvonen H.Monitoring and cordoning wildfires with an autonomous swarm of unmanned aerial vehicles.Drones2022;6:301

[56]

Madridano Á,Flores P,de la Escalera A.Software architecture for autonomous and coordinated navigation of uav swarms in forest and urban firefighting.Appl Sci2021;11:1258

[57]

Aydin B,Tao J.Use of fire-extinguishing balls for a conceptual system of drone-assisted wildfire fighting.Drones2019;3:17.

[58]

Alsammak ILH,Aris H,Mahdi MN.The use of swarms of unmanned aerial vehicles in mitigating area coverage challenges of forest-fire-extinguishing activities: a systematic literature review.Forests2022;13:811.

[59]

Bharany S,Frnda J.Wildfire monitoring based on energy efficient clustering approach for FANETS.Drones2022;6:193

[60]

Lee S.Decision support scheduling for maritime search and rescue planning with a system of UAVs and fuel service stations. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS). Denver, CO, USA; 2015.pp.1168-77.

[61]

Chen M,Xiong X,Chen Z.A maritime emergency search and rescue system based on unmanned aerial vehicle and its landing platform. In Proceedings of the 2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT). Qingdao, China; 2021.pp.758-61.

[62]

Cho S,Park H.Multi-UAV coverage path planning based on hexagonal grid decomposition in maritime search and rescue.Mathematics2022;10:83

[63]

Liu L,Li L.Research on maritime search and rescue recognition based on agent technology. In Proceedings of the 2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA); 2020 June 201-5; Tianjin, China.

[64]

Phadke A, Medrano, A. Drone2Drone: a search and rescue framework for finding lost UAV swarm agents. TAMUCC- Symposium for Student Innovation, Research, and Creative Activities Posters; 2023 April.

[65]

Queralta JP,Can Pullinen B.Collaborative multi-robot search and rescue: planning, coordination, perception, and active vision.IEEE Access2020;8:191617-43

[66]

Lygouras E,Taitzoglou A,Mitropoulos A.Unsupervised human detection with an embedded vision system on a fully autonomous UAV for search and rescue operations.Sensors2019;19:3542 PMCID:PMC6720834

[67]

Gianni M,Menna M.Adaptive robust three-dimensional trajectory tracking for actively articulated tracked vehicles*.J Field Robotics2016;33:901-30

[68]

Arnold R,Abruzzo B.Heterogeneous UAV multi-role swarming behaviors for search and rescue. In Proceedings of the Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). Victoria, BC, Canada; 2020.pp.122-8.

[69]

Ruetten L,Feil-Seifer D.Area-optimized UAV swarm network for search and rescue operations. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC);2020, pp. 0613-8

[70]

Lu Y,Wang J.Multi-population parallel wolf pack algorithm for task assignment of UAV swarm.Appl Sci2021;11:11996.

[71]

Xu S,Zhou Z,Huang J.A Task allocation strategy of the UAV swarm based on multi-discrete wolf pack algorithm.Appl Sci2022;12:1331

[72]

Lu Y,Wang J.Task assignment of UAV swarm based on wolf pack algorithm.Appl Sci2020;10:8335

[73]

Garg V,Shukla A.Comparative analysis of fruit fly-inspired multi-robot cooperative algorithm for target search and rescue. In Proceedings of the 2022 IEEE World Conference on Applied Intelligence and Computing (AIC); 2022 June 444-50.

[74]

Shi K,Xia S.Multiple swarm fruit fly optimization algorithm based path planning method for multi-UAVs.Appl Sci2020;10:2822

[75]

Luo R,Guo J.Solving the multi-functional heterogeneous UAV cooperative mission planning problem using multi-swarm fruit fly optimization algorithm.Sensors2020;20:5026 PMCID:PMC7570476

[76]

Chen B.Autonomous tactical deployment of the UAV array using self-organizing swarm intelligence.IEEE Consumer Electron Mag2020;9:52-6

[77]

Al-Kaff A,Moreno FM,Armingol JM.An appearance-based tracking algorithm for aerial search and rescue purposes.Sensors2019;19:652 PMCID:PMC6387277

[78]

Qi F,Li Z.Automatic air-to-ground recognition of outdoor injured human targets based on UAV Bimodal information: the explore study.Appl Sci2022;12:3457

[79]

Chatziparaschis D,Partsinevelos P.Aerial and ground robot collaboration for autonomous mapping in search and rescue missions.Drones2020;4:79

[80]

Chaves AN,Jose J.Adaptive search control applied to search and rescue operations using unmanned aerial vehicles (UAVs).IEEE Latin Am Trans2014;12:1278-83

[81]

Radoglou-grammatikis P,Lagkas T.A compilation of UAV applications for precision agriculture.Computer Networks2020;172:107148

[82]

Hattenberger G,Maury N.Field report: deployment of a fleet of drones for cloud exploration.Int J Micro Air Veh2022;14:175682932110708

[83]

Mansour HS,Aziz IA.Cross-layer and energy-aware AODV routing protocol for flying Ad-Hoc networks.Sustainability2022;14:8980

[84]

Chen S, Shi L, Ding X, Lv Z, Li Z. Energy efficient resource allocation and trajectory optimization in uav-assisted mobile edge computing system. In Proceedings of the 2021 7th International Conference on Big Data Computing and Communications (BigCom). Deqing, China; 2021.pp.7-13.

[85]

Yang Z,Wang K.Energy efficient resource allocation in UAV-enabled mobile edge computing networks.IEEE Trans Wireless Commun2019;18:4576-89

[86]

Lansky J,Malik MH.An energy-aware routing method using firefly algorithm for flying ad hoc networks.Sci Rep2023;13:1323 PMCID:PMC9873979

[87]

Raza W,Ferrini F.Energy-efficient inference on the edge exploiting TinyML capabilities for UAVs.Drones2021;5:127

[88]

Park S,Kim H.Energy-efficient topology control for UAV networks.Energies2019;12:4523

[89]

Sawalmeh A,Shakhatreh H.Efficient deployment of multi-UAVs in massively crowded events.Sensors2018;18:3640 PMCID:PMC6263925

[90]

Al-dosari K,Balachandran W.Systematic review on civilian drones in safety and security applications.Drones2023;7:210

[91]

Kwon D,Kim SY.A study on auto patrol drone development for safety management. In Proceedings of the Proceedings of the International Conference on Compute and Data Analysis. Lakeland, FL, USA; 2017.pp.293-7.

[92]

Yu Z,Gong S.Joint Task offloading and resource allocation in UAV-enabled mobile edge computing.IEEE Internet Things J2020;7:3147-59.

[93]

Alam MM.Joint topology control and routing in a UAV swarm for crowd surveillance.J Netw Comput Appl2022;204:103427

[94]

Xu C,Jiang Y,Yang T.Communication aware UAV swarm surveillance based on hierarchical architecture.Drones2021;5:33.

[95]

Chen Y,Shang X,Wang J.Multi-UAV autonomous path planning in reconnaissance missions considering incomplete information: a reinforcement learning method.Drones2023;7:10

[96]

Kashino Z,Benhabib B.A hybrid strategy for target search using static and mobile sensors.IEEE Trans Cybern2020;50:856-68

[97]

Qin C,Mihaylova LS.3, 2, 1, drones go! A testbed to take off UAV swarm intelligence for distributed sensing.arXiv2022;05914.

[98]

Hong Y,Kim S.Autonomous mission of multi-uav for optimal area coverage.Sensors2021;21:2482 PMCID:PMC8038291

[99]

Loureiro G,Martins A.Emergency landing spot detection algorithm for unmanned aerial vehicles.Remote Sens2021;13:1930

[100]

Santin R,Vivas A.Matheuristics for multi-UAV routing and recharge station location for complete area coverage.Sensors2021;21:1705 PMCID:PMC7958127

[101]

Hong I,Murray AT.A range-restricted recharging station coverage model for drone delivery service planning.Transport Res C-Emer2018;90:198-212

[102]

Balta H,Beglerovic H,Siciliano B.3D registration and integrated segmentation framework for heterogeneous unmanned robotic systems.Remote Sens2020;12:1608

[103]

Dewan A,Soni N. Optimization based coordinated UGV-MAV exploration for 2D augmented mapping. Available from: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=03c1ff959a47d215fe404fde769ca23ac5a74dda [Last accessed on 21 Sep 2023]

[104]

Mahendran A,Soni N.UGV-MAV collaboration for augmented 2D maps. In Proceedings of the Proceedings of Conference on Advances In Robotics; 2013.pp.1-6.

[105]

Jung S,Kim Y.Aerial Surveillance with low-altitude long-endurance tethered multirotor UAVs using photovoltaic power management system.Energies2019;12:1323

[106]

Zhu L,Li J,Yang Q.Connectivity-maintenance UAV formation control in complex environment.Drones2023;7:229

[107]

Stolfi DH.An evolutionary algorithm to optimise a distributed UAV swarm formation system.Appl Sci2022;12:10218

[108]

Luo Y,Yang J,Leng S.Auction mechanism-based multi-type task planning for heterogeneous UAVs swarm. In Proceedings of the International Conference on Communication Technology; 2020.pp. 698-702.

[109]

Huang R,Vaughan R.Active image-based modeling with a toy drone. 2018 IEEE International Conference on Robotics and Automation (ICRA); 2018.pp. 6124-31.

[110]

Power W,Saranovic D,Obradovic Z.Autonomous navigation for drone swarms in GPS-denied environments using structured learning. In Artificial Intelligence Applications and Innovations; IFIP Advances in Information and Communication Technology; 2020.pp. 219-31.

[111]

Sarras I,Bertrand S.Collaborative multiple micro air vehicles’ localization and target tracking in GPS-denied environment from range-velocity measurements.International Journal of Micro Air Vehicles2018;10:225-39.

[112]

Chen S,Pang T.Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs.PLoS One2023;18:e0282333 PMCID:PMC10062595

[113]

Basiri A,Glielmo L.Improving visual SLAM by combining SVO and ORB-SLAM2 with a complementary filter to enhance indoor mini-drone localization under varying conditions.Drones2023;7:404

[114]

Ekici M,Özek A.Warehouse drone: indoor positioning and product counter with virtual fiducial markers.Drones2023;7:3

[115]

Qamar S,Arshad MA,Khan A.Autonomous drone swarm navigation and multi-target tracking in 3d environments with dynamic obstacles.arXiv2022;06253

[116]

Jia Y,Zhang Z.Accelerating emergence of aerial swarm.Appl Sci2020;10:7986

[117]

Smith P,Aleti A.Adaptive data transfer methods via policy evolution for UAV swarms. In Proceedings of the International Telecommunication Networks and Applications Conference (ITNAC). Melbourne, VIC, Australia; 2017.pp.1-8.

[118]

Wang K,Qiao X.Adjustable fully adaptive cross-entropy algorithms for task assignment of multi-UAVs.Drones2023;7:204

[119]

Khan S,Khan P,Khan A.An ant-hocnet routing protocol based on optimized fuzzy logic for swarm of UAVs in FANET.Wirel Commun Mob Com2022;2022:1-12

[120]

Sun Q,Zhang Y,Liu C.A baseline assessment method of UAV swarm resilience based on complex networks. In Proceedings of the 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI); 2021.pp. 000083-6.

[121]

Shen H,Lu H,Tian B.A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments.Chinese J Aeronaut2022;35:59-69.

[122]

Federal Aviation Administration. Remote identification of unmanned aircraft-final rule. Available from: https://www.faa.gov/newsroom/remoteid-final-rule [Last accessed on 25 Sep 2023]

[123]

Phadke A,Medrano FA.Navigating the skies: examining the FAA’s remote identification rule for unmanned aircraft systems.Drone Syst Appl2023;11:1-4

[124]

Petkovics I,Petkovics A.IoT devices vs. drones for data collection in agriculture. In DAAAM International Scientific Book 2017; 2017; pp. 063-80.

[125]

Huang S,Kwan JLP,Dymkou SM.Distributed UAV loss detection and auto-replacement protocol with guaranteed properties.J Intell Robot Syst2019;93:303-16

[126]

Zhao X,Qiu Q.Designing two-level rescue depot location and dynamic rescue policies for unmanned vehicles.Reliab Eng Syst Safe2023;233:109119

[127]

Gomes J,Christensen AL. Cooperative coevolution of partially heterogeneous multiagent systems. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems; Istanbul; 2015.pp. 297-305. Available from: https://dl.acm.org/doi/abs/10.5555/2772879.2772919# [Last accessed on 25 Sep 2023]

[128]

Deka A.Natural emergence of heterogeneous strategies in artificially intelligent competitive teams.ASI2021; 12689:13-25

[129]

Incze ML,Gagner C.Communication and collaboration among heterogeneous unmanned systems using SAE Jaus standard formats and protocols..IFAC-PapersOnLine2015;48:7-10

[130]

Xue K.Distributed consensus of USVs under heterogeneous UAV-USV multi-agent systems cooperative control scheme.J Mar Sci Eng2021;9:1314

AI Summary AI Mindmap
PDF

168

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/