[1]ZHANG Lin,XIE Kaixin,ZHENG Xianhua,et al.A survey on perception ability and control architecture of multi-robot system[J].CAAI Transactions on Intelligent Systems,2024,19(4):767-790.[doi:10.11992/tis.202306001]
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A survey on perception ability and control architecture of multi-robot system

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 767-790 Column: 综述 Public date: 2024-07-05
Title:
A survey on perception ability and control architecture of multi-robot system
Author(s):
ZHANG Lin1; 2;  XIE Kaixin1;  ZHENG Xianhua3;  SONG Yongshi3;  WANG Chengjun1;  ZHAO Yanzheng2
1. School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China;
2. School of Mechanical and Power Engineering, Shanghai Jiao tong University, Shanghai 200240, China;
3. School of Robotics Engineering, Yangtze Normal University, Chongqing 408100, China
Keywords:
multi-robot system; mobile robot; drone; unmanned ship; underwater robot; perception; control; synergy
CLC:
TP18
DOI:
10.11992/tis.202306001
Abstract:
To facilitate the intelligent and unmanned development of multi-robot systems(MRS) and improve their detection capabilities and flexibility in various working environments, beginning with the perspective of MRS’s perception abilities and their control system architecture, this paper presents a thorough investigation and analysis of related research and work on MRS, with a particular focus on exploring the perception abilities and control system architecture of MRS in four application environments: aerial, ground, surface, and underwater. Additionally, it offers a forward-looking perspective on future research directions. The findings of this paper can provide guidance for the subsequent selection of perception methods and control systems in MRS.
References:
[1] CAO Yan, WEI Wanyu, BAI Yu, et al. Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm[J]. Cluster computing, 2019, 22(S3): 5175–5184.
[2] BEG A, QURESHI A R, SHELTAMI T, et al. UAV-enabled intelligent traffic policing and emergency response handling system for the smart city[J]. Personal and ubiquitous computing, 2021, 25(1): 33–50.
[3] ERGEZER H, LEBLEBICIO?LU K. 3D path planning for multiple UAVs for maximum information collection[J]. Journal of intelligent & robotic systems, 2014, 73(1-4): 737–762.
[4] GEORGE J, SUJIT P B, SOUSA J B. Search strategies for multiple uav search and destroy missions[J]. Journal of intelligent & robotic systems, 2011, 61(1-4): 355–367.
[5] APOSTOLIDIS S D, KAPOUTSIS P C, KAPOUTSIS A C, et al. Cooperative multi-UAV coverage mission planning platform for remote sensing applications[J]. Autonomous robots, 2022, 46(2): 373–400.
[6] MAZA I, KONDAK K, BERNARD M, et al. Multi- UAV cooperation and control for load transporta- tion and deployment[J]. Journal of intelligent and robotic systems, 2010, 57(1-4): 417–449.
[7] PENG Zhouhua, WANG Dan, LI Tieshan. Predictor-based neural dynamic surface control for distributed formation tracking of multiple marine surface vehicles with improved transient performance[J]. Science China information sciences, 2016, 59(9): 92210.
[8] XIE Heng, DONG Kaixu, CHIRARATTANANON P. Cooperative transport of a suspended payload via two aerial robots with inertial sensing[J]. IEEE access, 2022, 10: 81764–81776.
[9] ZHU Lihua, WANG Yu, WU Zhiqiang. An adaptive priority allocation for formation UAVs in complex context[J]. IEEE transactions on aerospace and electronic systems, 2021, 57(2): 1002–1015.
[10] DENTLER J, ROSALIE M, DANOY G, et al. Collision avoidance effects on the mobility of a UAV swarm using chaotic ant colony with model predictive control[J]. Journal of intelligent & robotic systems, 2019, 93(1-2): 227–243.
[11] ARBANAS B, IVANOVIC A, CAR M, et al. Decentralized planning and control for UAV-UGV cooperative teams[J]. Autonomous robots, 2018, 42(8): 1601–1618.
[12] PAN Yan, LI Shining, ZHANG Xiao, et al. Directional monitoring of multiple moving targets by multiple unmanned aerial vehicles[C]//2017 IEEE Global Communications Conference. Singapore: IEEE, 2017: 1-6.
[13] FARMANI N, SUN L, PACK D. Tracking multiple mobile targets using cooperative Unmanned Aerial Vehicles[C]//2015 International Conference on Unmanned Aircraft Systems. Denver: IEEE, 2015: 395-400.
[14] LI Chunyu, WANG Zidong, SONG Weihao, et al. Resilient unscented Kalman filtering fusion with dynamic event-triggered scheme: applications to multiple unmanned aerial vehicles[J]. IEEE transactions on control systems technology, 2023, 31(1): 370–381.
[15] CAPITAN J, MERINO L, OLLERO A. Cooperative decision-making under uncertainties for multi-target surveillance with multiples UAVs[J]. Journal of intelligent & robotic systems, 2016, 84(1-4): 371–386.
[16] KOTHARI M, SHARMA R, POSTLETHWAITE I, et al. Cooperative target-capturing with incomplete target information[J]. Journal of intelligent & robotic systems, 2013, 72(3-4): 373–384.
[17] DULCE-GALINDO J A, SANTOS M A, RAFFO G V, et al. Distributed supervisory control for multi-plerobot autonomous navigation performing single-robot tasks[J]. Mechatronics, 2022, 86: 102848.
[18] QIAN Moshu, ZHANG Zhen, ZHENG Zhong, et al. Sliding mode control-based distributed fault tolerant tracking control for multiple unmanned aerial vehicles with input constraints and actuator faults[J]. International journal of robust and nonlinear control, 2022.
[19] CHEN Xi, QIN Kaiyu, LOU Xuan, et al. Distributed motion control of UAVs for cooperative target location under compound constraints[C]//2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing. Chengdu: IEEE, 2021: 576-580.
[20] CHEN Xia, ZHANG Chong. The method of multi unmanned aerial vehicle cooperative tracking in formation based on the theory of consensus[C]//2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics. Hangzhou: IEEE, 2013: 148-151.
[21] PHAM H X, LA H M, FEIL-SEIFER D, et al. A distributed control framework of multiple unmanned aerial vehicles for dynamic wildfire tracking[J]. IEEE transactions on systems, man, and cybernetics:systems, 2020, 50(4): 1537–1548.
[22] HU Chaofang, MENG Zhou, QU Ge, et al. Distributed cooperative path planning for tracking ground moving target by multiple fixed-wing UAVs via DMPC-GVD in urban environment[J]. International journal of control, automation and systems, 2021, 19(2): 823–836.
[23] ZHANG Boyang, SUN Xiuxia, LIU Shuguang, et al. Distributed fault tolerant model predictive control for multi‐unmanned aerial vehicle system[J]. Asian journal of control, 2022, 24(3): 1273–1292.
[24] BRAND?O A S, SARCINELLI-FILHO M. On the guidance of multiple UAV using a centralized formation control scheme and delaunay triangulation[J]. Journal of intelligent & robotic systems, 2016, 84(1-4): 397–413.
[25] VILLA D K D, BRANDAO A S, SARCINELLI- FILHO M. Rod-shaped payload transportation using multiple quadrotors[C]//2019 International Conference on Unmanned Aircraft Systems. Atlanta: IEEE, 2019: 1036-1040.
[26] LIERET M, KOGAN V, DOLL S, et al. Automated in-house transportation of small load carriers with autonomous unmanned aerial vehicles[C]//2019 IEEE 15th International Conference on Automation Science and Engineering. Vancouver: IEEE, 2019: 1010-1015.
[27] KEYVAN M, JAFARINASAB M, SIROUSPOUR S, et al. Decentralized motion control in a cabled-based multi-drone load transport system[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems. Madrid: IEEE, 2018: 4198-4203.
[28] BACELAR T, MADEIRAS J, MELICIO R, et al. On-board implementation and experimental validation of collaborative transportation of loads with multiple UAVs[J]. Aerospace science and technology, 2020, 107: 106284.
[29] OMAR M, XU L, XIE X. Formation flight control of networked-delayed quadrotors for cooperative slung load transportation[C]//2022 IEEE International Conference on Mechatronics and Automation. Guilin: IEEE, 2022: 526-531.
[30] SHIRANI B, NAJAFI M, IZADI I. Cooperative load transportation using multiple UAVs[J]. Aerospace science and technology, 2019, 84: 158–169.
[31] ABHINAY N S, SHOBHIT S, SRIDHAR N, et al. Cooperative aerial slung load transportation using a novel adaptive sliding mode controller[C]//2021 International Conference on Unmanned Aircraft Systems. Athens: IEEE, 2021: 804-813.
[32] THAPA S, BAI H, ACOSTA J á. Cooperative aerial manipulation with decentralized adaptive force-consensus control[J]. Journal of intelligent & robotic systems, 2020, 97(1): 171–183.
[33] AGHDAM A S, MENHAJ M B, BARAZANDEH F, et al. Cooperative load transport with movable load center of mass using multiple quadrotor UAVs[C]//2016 4th International Conference on Control, Instrumentation, and Automation. Qazvin: IEEE, 2016: 23-27.
[34] LIN Min, CHEN Yuming, HAN Rui, et al. Discrete optimization on truck-drone collaborative transportation system for delivering medical resources[J]. Discrete dynamics in nature and society, 2022, 2022: 1–13.
[35] MOHIUDDIN A, ZWEIRI Y, ALMADHOUN R, et al. Energy distribution in Dual-UAV collaborative transportation through load sharing[J]. Journal of mechanisms and robotics, 2020: 1-14.
[36] RAVANKAR A, RAVANKAR A, KOBAYASHI Y, et al. Autonomous mapping and exploration with unmanned aerial vehicles using low cost sensors[C]//5th International Electronic Conference on Sensors and Applications. Basel Switzerland: MDPI, 2018: 44.
[37] HORYNA J, BACA T, WALTER V, et al. Decentralized swarms of unmanned aerial vehicles for search and rescue operations without explicit communication[J]. Autonomous robots, 2023, 47(1): 77–93.
[38] YOKOYAMA C, TAKIMOTO M, KAMBAYASHI Y. Cooperative control of multi-robots using mobile agents in a three-dimensional environment[C]//2013 IEEE International Conference on Systems, Man, and Cybernetics. Manchester: IEEE, 2013: 1115-1120.
[39] STEINER J A, BOURNE J R, HE Xiang, et al. Chemical-source localization using a swarm of decentralized unmanned aerial vehicles for urban/suburban environments[C]//Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and. Park City: ASME, 2019.
[40] PACE P, ALOI G, CALICIURI G, et al. A mission-oriented coordination framework for teams of mobile aerial and terrestrial smart objects[J]. Mobile networks and applications, 2016, 21(4): 708–725.
[41] ZHU Man, WEN Yuanqiao. Design and analysis of collaborative unmanned surface-aerial vehicle cruise systems[J]. Journal of advanced transportation, 2019, 2019: 1–10.
[42] GAO Chen, ZHEN Ziyang, GONG Huajun. A self-organized search and attack algorithm for multiple unmanned aerial vehicles[J]. Aerospace science and technology, 2016, 54: 229–240.
[43] DE MORAES R S, DE FREITAS E P. Distributed control for groups of unmanned aerial vehicles performing surveillance missions and providing relay communication network services[J]. Journal of intelligent & robotic systems, 2018, 92(3-4): 645–656.
[44] YAO Peng, WANG Xiaodong, YI Ke. Optimal search for marine target using multiple unmanned aerial vehicles[C]//2018 37th Chinese Control Conference. Wuhan: IEEE, 2018: 4552-4556.
[45] CHEN Linjie, LIU Qiankun, YANG Yifei, et al. Cooperative search self-organizing strategy for multiple unmanned aerial vehicles based on probability map and uncertainty map[C]//2020 Chinese Control And Decision Conference. Hefei: IEEE, 2020: 2685-2690.
[46] WANG Tianyu, GU Wei. UAV deployment with grid modeling and adaptive multiple pruning search in complex forest scenarios[J]. Wireless networks, 2021.
[47] LIU Zhong, GAO Xiaoguang, FU Xiaowei. A cooperative search and coverage algorithm with controllable revisit and connectivity maintenance for multiple unmanned aerial vehicles[J]. Sensors, 2018, 18(5): 1472.
[48] JI Xiaoting, WANG Xiangke, NIU Yifeng, et al. Cooperative search by multiple unmanned aerial vehicles in a nonconvex environment[J]. Mathematical problems in engineering, 2015, 2015: 1–19.
[49] YUE Wei, LI Chaofen, LIU Zhongchang, et al. A new search scheme using multi-bee-colony elite learning method for unmanned aerial vehicles in unknown environments[J]. Optimal control applications and methods, 2022, 43(6): 1645–1664.
[50] GARG V. Cooperative multi-robot target searching and tracking using velocity inspired robotic fruit fly algorithm[J]. SN computer science, 2021, 2(6): 474.
[51] JIANG Chao, CHEN Zhou, GUO Yi. Learning decentralized control policies for multi-robot formation[C]//2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Hong Kong: IEEE, 2019: 758-765.
[52] LUO Cai, ESPINOSA A P, PRANANTHA D, et al. Multi-robot search and rescue team[C]//2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. Kyoto: IEEE, 2011: 296-301.
[53] ANDERSON M, PAPANIKOLOPOULOS N. Implicit cooperation strategies for multi-robot search of unknown areas[J]. Journal of intelligent and robotic systems, 2008, 53(4): 381–397.
[54] WAHID N, ZAMZURI H, AMER N H, et al. Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler[J]. IET intelligent transport systems, 2020, 14(10): 1200–1209.
[55] TATSUMI S, MURAKAMI T. Compliance control during cooperative transport using multiple mobile robots[J]. 2020, 588: 787-793.
[56] MENDIBURU F J, MORAIS M R A, LIMA A M N. Behavior coordination in multi-robot systems[C]//2016 IEEE International Conference on Automatica. Curico: IEEE, 2016: 1-7.
[57] BAI Chengchao, YAN Peng, PAN Wei, et al. Learning-based multi-robot formation control with obstacle avoidance[J]. IEEE transactions on intelligent transportation systems, 2022, 23(8): 11811–11822.
[58] RAVANKAR A, RAVANKAR A A, KOBAYASHI Y, et al. Hitchhiking based symbiotic multi-robot navigation in sensor networks[J]. Robotics, 2018, 7(3): 37.
[59] ZHOU Fan, DONG Bo, LI Yuanchun. Torque sens- orless force/position decentralized control for const- rained reconfigurable manipulator with harmonic drive transmission[J]. International journal of control, automation and systems, 2017, 15(5): 2364–2375.
[60] PENG Yuanchih, CARABIS D S, WEN J T. Collab- orative manipulation with multiple dualarm robots under human guidance[J]. International journal of intelligent robotics and applications, 2018, 2(2): 252–266.
[61] RAUNIYAR A, UPRETI H C, MISHRA A, et al. MeWBots: mecanum-wheeled robots for collaborative manipulation in an obstacle-clustered environment without communication[J]. Journal of intelligent & robotic systems, 2021, 102(1): 3.
[62] HICHRI B, FAUROUX J C, ADOUANE L, et al. Design of cooperative mobile robots for co-manipulation and transportation tasks[J]. Robotics and computer-integrated manufacturing, 2019, 57: 412–421.
[63] AMANATIADIS A, HENSCHEL C, BIRKICHT B, et al. AVERT: An autonomous multi-robot system for vehicle extraction and transportation[C]//2015 IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2015: 1662-1669.
[64] YAN Zhi, JOUANDEAU N, ALI-CHERIF A. Multi -robot heuristic goods transportation[C]//2012 6th IEEE International Conference Intel-Ligent Systems. Sofia: IEEE, 2012: 409-414.
[65] HU Zhixian, ZHAO Zhixiang, ZHANG Lianxin, et al. Collaborative object transportation by multiple robots with onboard object localization algorithm[C]//2019 IEEE International Conference on Robotics and Biomimetics. Dali: IEEE, 2019: 2344-2350.
[66] MANKO S V, DIANE S A K, KRIVOSHATSKIY A E, et al. Adaptive control of a multi-robot system for transportation of large-sized objects based on reinforcement learning[C]//2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. Moscow and St. Petersburg: IEEE, 2018: 923-927.
[67] ZHANG Tianrui, XU Jianan, WU Baoku. Hybrid path planning model for multiple robots considering obstacle avoidance[J]. IEEE access, 2022, 10: 71914–71935.
[68] HARIK E H C, GUERIN F, GUINAND F, et al. UAV-UGV cooperation for objects transportation in an industrial area[C]//2015 IEEE International Conference on Industrial Technology. Seville: IEEE, 2015: 547-552.
[69] HABIBI G, KINGSTON Z, XIE W, et al. Distributed centroid estimation and motion controllers for collective transport by multi-robot systems[C]//2015 IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2015: 1282-1288.
[70] YANG X, WATANABE K, IZUMI K, et al. A decentralized control system for cooperative transportation by multiple non-holonomic mobile robots[J]. International journal of control, 2004, 77(10): 949–963.
[71] FARIVARNEJAD H, WILSON S, BERMAN S. Decentralized sliding mode control for autonomous collective transport by multi-robot systems[C]//2016 IEEE 55th Conference on Decision and Control. Las Vegas: IEEE, 2016: 1826-1833.
[72] ZHANG Lin, SUN Yufeng, BARTH A, et al. Decentralized control of multi-robot system in cooperative object transportation using deep reinforcement learning[J]. IEEE access, 2020, 8: 184109–184119.
[73] KASHIWAZAKI K, YONEZAWA N, ENDO M, et al. A car transportation system using multiple mobile robots: iCART II[C]//2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Francisco: IEEE, 2011: 4593-4600.
[74] DAI Yanyan, KIM Y, WEE S, et al. Symmetric caging formation for convex polygonal object transportation by multiple mobile robots based on fuzzy sliding mode control[J]. ISA transactions, 2016, 60: 321–332.
[75] NAVARRO-ALARCON D, LIU Y H. Fourier-based shape servoing: a new feedback method to actively deform soft objects into desired 2-D image contours[J]. IEEE transactions on robotics, 2018, 34(1): 272–279.
[76] VO C D, DANG D A, LE P H. Development of multi-robotic arm system for sorting system using computer vision[J]. Journal of robotics and control, 2022, 3(5): 690–698.
[77] NAVARRO-ALARCON D, LIU Y H, ROMERO J G, et al. Model-free visually servoed deformation control of elastic objects by robot manipulators[J]. IEEE transactions on robotics, 2013, 29(6): 1457–1468.
[78] LI Yinxiao, WANG Yan, YUE Yonghao, et al. Model-driven feedforward prediction for manipulation of deformable objects[J]. IEEE transactions on automation science and engineering, 2018, 15(4): 1621–1638.
[79] HE BAI, WEN J T. Cooperative load transport: a formation-control perspective[J]. IEEE transactions on robotics, 2010, 26(4): 742–750.
[80] DELGADO A, CORRALES J A, MEZOUAR Y, et al. Tactile control based on Gaussian images and its application in bi-manual manipulation of deformable objects[J]. Robotics and autonomous systems, 2017, 94: 148–161.
[81] KRUSE D, RADKE R J, WEN J T. Collaborative human-robot manipulation of highly deformable materials[C]//2015 IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2015: 3782-3787.
[82] HIGASHIMORI M, YOSHIMOTO K, KANEKO M. Active shaping of an unknown rheological object based on deformation decomposition into elasticity and plasticity[C]//2010 IEEE International Conference on Robotics and Automation. Anchorage: IEEE, 2010: 5120-5126.
[83] HE Yanhao, WU Min, LIU Steven. A cooperative optimization strategy for distributed multi-robot manipulation with obstacle avoidance and internal performance maximization[J]. Mechatronics, 2021, 76: 102560.
[84] DI LILLO P, PIERRI F, ANTONELLI G, et al. A framework for set-based kinematic control of multi-robot systems[J]. Control engineering practice, 2021, 106: 104669.
[85] HE Yuhong, LI Xiaoxiao, XU Zhihao, et al. Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks[J]. Neurocomputing, 2021, 456: 1–10.
[86] JIN Long, LI Shuai, LUO Xin, et al. Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arm [C]//2017 International Joint Conference on Neural Networks. Anchorage: IEEE, 2017: 4165-4170.
[87] HA Huy, XU Jingxi, SONG Shuran. Learning a decentralized multi-arm motion planner[EB/OL]. (2020-11-05)[2023-04-01]. https://doi.org/10.48550/arXiv.2011.02608.
[88] LIU Mei, ZHANG Jiazheng, SHANG Mingsheng. Real-time cooperative kinematic control for multiple robots in distributed scenarios with dynamic neural networks[J]. Neurocomputing, 2022, 491: 621–632.
[89] BASILE F, CACCAVALE F, CHIACCHIO P, et al. Task-oriented motion planning for multi-arm robotic systems[J]. Robotics and computer-integrated manufacturing, 2012, 28(5): 569–582.
[90] ALONSO-MORA J, KNEPPER R, SIEGWART R, et al. Local motion planning for collaborative multi-robot manipulation of deformable objects[C]//2015 IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2015: 5495-5502.
[91] LIN Juntong, YANG Xuyun, ZHENG Peiwei, et al. End-to-end decentralized multi-robot navigation in unknown complex environments via deep reinfor- cement learning[C]//2019 IEEE International Conference on Mechatronics and Automation. Tianjin: IEEE, 2019: 2493-2500.
[92] FU Junjie, LUV Yuezu, WEN Guanghui, et al. Local measurement based formation navigation of nonh- olonomic robots with globally bounded inputs and collision avoidance[J]. IEEE transactions on network science and engineering, 2021, 8(3): 2342–2354.
[93] CHEN Wenzhou, ZHOU Shizheng, PAN Zaisheng, et al. Mapless collaborative navigation for a multi-robot system based on the deep reinforcement learning[J]. Applied sciences, 2019, 9(20): 4198.
[94] COGNETTI M, ORIOLO G, PELITI P, et al. Coop- erative control of a heterogeneous multi-robot system based on relative localization[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago: IEEE, 2014: 350-356.
[95] PANDEY A, KASHYAP A K, PARHI D R, et al. Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture[J]. World journal of engineering, 2019, 16(2): 275–286.
[96] WANG Mingyu, SCHWAGER M. Distributed collision avoidance of multiple robots with probabilistic buffered voronoi cells[C]//2019 International Symposium on Multi-Robot and Multi-Agent Systems. New Brunswick: IEEE, 2019: 169-175.
[97] CHEN Guangda, YAO Shunyi, MA Jun, et al. Distributed non-communicating multi-robot collision avoidance via map-based deep reinforcement learning[J]. Sensors, 2020, 20(17): 4836.
[98] HAN Ruihua, CHEN Shengduo, WANG Shuaijun, et al. Reinforcement learned distributed multi-robot navigation with reciprocal velocity obstacle shaped rewards[J]. IEEE robotics and automation letters, 2022, 7(3): 5896–5903.
[99] FU Junjie, WEN Guanghui, YU Xinghuo, et al. Distributed formation navigation of constrained second-order multiagent systems with collision avoidance and connectivity maintenance[J]. IEEE transactions on cybernetics, 2022, 52(4): 2149–2162.
[100] ZHAI Yuanzhao, DING Bo, LIU Xuan, et al. Decentralized multi-robot collision avoidance in complex scenarios with selective communication[J]. IEEE robotics and automation letters, 2021, 6(4): 8379–8386.
[101] ZHU Hai, ALONSO-MORA J. B-UAVC: buffered uncertainty-aware voronoi cells for probabilistic multi-robot collision avoidance[C]//2019 International Symposium on Multi-Robot and Multi-Agent Systems. New Brunswick: IEEE, 2019: 162-168.
[102] BALCH T, ARKIN R C. Behavior-based formation control for multirobot teams[J]. IEEE transactions on robotics and automation, 1998, 14(6): 926–939.
[103] ZHOU Huaidong, FENG Pengbo, CHOU Wusheng. A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment[J]. Industrial robot: the international journal of robotics research and application, 2023, 50(1): 94–106.
[104] HACENE N, MENDIL B. Behavior-based autonomous navigation and formation control of mobile robots in unknown cluttered dynamic environments with dynamic target tracking[J]. International journal of automation and computing, 2021, 18(5): 766–786.
[105] CHINNAIAH M C, SAVITRI T S, KUMAR P R. A novel approach in navigation of FPGA robots in robust indoor environment[C]//2015 International Conference on Advanced Robotics and Intelligent Systems. Taipei: IEEE, 2015: 1-6.
[106] WANG Dongshu, WANG Haitao, LIU Lei. Unknown environment exploration of multi-robot system with the FORDPSO[J]. Swarm and evolutionary computation, 2016, 26: 157–174.
[107] DUBE R, GAWEL A, SOMMER H, et al. An online multi-robot SLAM system for 3D LiDARs[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver: IEEE, 2017: 1004-1011.
[108] YASUDA G. Behavior-based autonomous cooperative control of intelligent mobile robot systems with embedded Petri nets[C]//2014 Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems. Kitakyushu: IEEE, 2014: 1085-1090.
[109] CORAH M, O’MEADHRA C, GOEL K, et al. Communication-efficient planning and mapping for multi-robot exploration in large environments[J]. IEEE robotics and automation letters, 2019, 4(2): 1715–1721.
[110] FUNG N, ROGERS J, NIETO C, et al. Coordinating multi-robot systems through environment partitioning for adaptive informative sampling[C]//2019 International Conference on Robotics and Automation. Montreal: IEEE, 2019: 3231-3237.
[111] HU Junyan, NIU Hanlin, CARRASCO J, et al. Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning[J]. IEEE transactions on vehicular technology, 2020, 69(12): 14413–14423.
[112] TANG Hongwei, SUN Wei, LIN Anping, et al. A GWO-based multi-robot cooperation method for target searching in unknown environments[J]. Expert systems with applications, 2021, 186: 115795.
[113] LI Jie, TAN Ying. A probabilistic finite state machine based strategy for multi-target search using swarm robotics[J]. Applied soft computing, 2019, 77: 467–483.
[114] KABIR R H, LEE K. Efficient, decentralized, and collaborative multi-robot exploration using optimal transport theory[C]//2021 American Control Conference. New Orleans: IEEE, 2021: 4203-4208.
[115] JIA Chengcheng, YANG Yajun, YANG Xuerong. Distributed area coverage control for multi-agent based on artificial potential field[C]//2019 IEEE International Conference on Unmanned Systems. Beijing: IEEE, 2019: 314-317.
[116] GENG Mingyang, XU Kele, ZHOU Xing, et al. Learning to cooperate via an attention-based communication neural network in decentralized multi-robot exploration[J]. Entropy, 2019, 21(3): 294.
[117] RISTIC B, ANGLEY D, MORAN B, et al. Autonomous multi-robot search for a hazardous source in a turbulent environment[J]. Sensors, 2017, 17(4): 918.
[118] WANG Wei, WANG Zijian, MATEOS L, et al. Distributed motion control for multiple connected surface vessels[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas: IEEE, 2020: 11658-11665.
[119] CHEN Yaojie, XIANG Shangshang, CHEN feixiang. Research on a task planning method for multi-ship cooperative driving[J]. Journal of Shanghai Jiaotong University (Science), 2019, 24(2): 233–242.
[120] FARINELLI A, RAEISSI M M, MARCHI N, et al. Interacting with team oriented plans in multi-robot systems[J]. Autonomous agents and multi-agent systems, 2017, 31(2): 332–361.
[121] LIU Chenguang, QI Junlin, CHU Xiumin, et al. Cooperative ship formation system and control methods in the ship lock waterway[J]. Ocean engineering, 2021, 226: 108826.
[122] MOULTON J, KARAPETYAN N, BUKHSBAUM S, et al. An autonomous surface vehicle for long term operations[C]//OCEANS 2018 MTS/IEEE Charleeston. Charleston: IEEE, 2018: 0197-7385.
[123] CHARALAMBOPOULOS N, NEARCHOU A C. Ship routing using genetic algorithms[J]. Operations research forum, 2021, 2(3): 45.
[124] YANG Xiaofei, SHI Yilun, LIU Wei et al. Global path planning algorithm based on double DQN for multi-tasks amphibious unmanned surface vehicle[J]. Ocean engineering, 2022, 266: 112809.
[125] HONG Xiaobin, CUI Bin, CHEN Weiguo, et al. Research on multi-ship target detection and tracking method based on camera in complex scenes[J]. Journal of marine science and engineering, 2022, 10(7): 978.
[126] FEFILATYEV S, GOLDGOF D, SHREVE M, et al. Detection and tracking of ships in open sea with rapidly moving buoy-mounted camera system[J]. Ocean engineering, 2012, 54: 1–12.
[127] ALMEIDA J, SILVESTRE C, PASCOAL A. Cooperative control of multiple surface vessels in the presence of ocean currents and parametric model uncertainty[J]. International journal of robust and nonlinear control, 2010, 20(14): 1549–1565.
[128] ZHANG Yunpeng, XING Mengdao, ZHANG Jinsong, et al. Robust multi-ship tracker in sar imagery by fusing feature matching and modified KCF[J]. IEEE geoscience and remote sensing letters, 2023, 20: 1–5.
[129] HU Yingjun, ZHANG Anmin, TIAN Wuliu, et al. Multi-ship collision avoidance decision-making based on collision risk index[J]. Journal of marine science and engineering, 2020, 8(9): 640.
[130] XIONG Jianbin, SHU Lei, WANG Qinruo, et al. A scheme on indoor tracking of ship dynamic positioning based on distributed multi-sensor data fusion[J]. IEEE access, 2017, 5: 379–392.
[131] ZHENG Rongcai, XIE Hongwei, YUAN Kexin. Autonomous collision avoidance system in a multi-ship environment based on proximal policy optimization method[J]. Ocean engineering, 2023, 272: 113779.
[132] NIELSEN U D, BRODTKORB A H, S?RENSEN A J. Sea state estimation using multiple ships simultaneously as sailing wave buoys[J]. Applied ocean research, 2019, 83: 65–76.
[133] CUI Zongyong, LI Qi, CAO Zongjie, et al. Dense attention pyramid networks for multi-scale ship detection in SAR images[J]. IEEE transactions on geoscience and remote sensing, 2019, 57(11): 8983–8997.
[134] LI Shijie, LIU Jialun, NEGENBORN R R. Distributed coordination for collision avoidance of multiple ships considering ship maneuverability[J]. Ocean engineering, 2019, 181: 212–226.
[135] DO K D. Synchronization motion tracking control of multiple underactuated ships with collision avoidance[J]. IEEE transactions on industrial electronics, 2016, 63(5): 2976–2989.
[136] ZHANG Jinfen, ZHANG Di, YAN Xinping, et al. A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs[J]. Ocean engineering, 2015, 105: 336–348.
[137] DO K D. Practical formation control of multiple underactuated ships with limited sensing ranges[J]. Robotics and autonomous systems, 2011, 59(6): 457–471.
[138] WANG Tengfei, WU Qing, ZHANG Jinfen, et al. Autonomous decision-making scheme for multi-ship collision avoidance with iterative observation and inference[J]. Ocean engineering, 2020, 197: 106873.
[139] WANG Tengfei, YAN Xinping, WANG Yang, et al. A distributed model predictive control using virtual field force for multi-ship collision avoidance under COLREGs[C]//2017 4th International Conference on Transportation Information and Safety. Banff: IEEE, 2017: 296-305.
[140] LIU Y, BUCKNALL R. Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment[J]. Oceanengineering, 2015, 97: 126–144.
[141] BRUZZONE G, BIBULI M, ZEREIK E, et al. Cooperative adaptive guidance and control paradigm for marine robots in an emergency ship towing scenario[J]. International journal of adaptive control and signal processing, 2017, 31(4): 562–580.
[142] LIU Wang, LYU Xiafu. Distributed cooperative formation of multiple unmanned ships based on KF[J]. Journal of physics: conference series, 2021, 1754(1): 012097.
[143] LI Shuai, GUO Yi, BINGHAM B. Multi-robot cooperative control for monitoring and tracking dynamic plumes[C]//2014 IEEE International Conference on Robotics and Automation. Hong Kong: IEEE, 2014: 67-73.
[144] SUN Ting, LIU Cheng, WANG Xuegang. Distributed anti-windup NN-sliding mode formation control of multi-ships with minimum cost[J]. ISA transactions, 2023, 138: 49-62
[145] SUN Xiaoming, GE Shuzhi, ZHANG Jun, et al. Adaptive region tracking control for multi-ships coordination[J]. IFAC proceedings volumes, 2013, 46(20): 39–44.
[146] WANG Kai, LI Jiayuna, YAN Xinping, et al. A novel bi-level distributed dynamic optimization method of ship fleets energy consumption[J]. Ocean engineering, 2020, 197: 106802.
[147] ROSS J, LINDSAY J, GREGSON E, et al. Collaboration of multi-domain marine robots towards above and below-water characterization of floating targets[C]//2019 IEEE International Symposium on Robotic and Sensors Environments. Ottawa: IEEE, 2019: 1-7.
[148] FENG Zhi, HU Guoqiang. Formation tracking control for a team of networked underwater robot systems with uncertain hydrodynamics[C]//2018 IEEE International Conference on Real-time Computing and Robotics. Kandima: IEEE, 2018: 372-377.
[149] SHI Liwei, BAO Pengxiao, GUO Shuxiang, et al. Underwater formation system design and implement for small spherical robots[J]. IEEE systems journal, 2023, 17(1): 1259–1269.
[150] PHAM H A, SORIANO T, NGO V H, et al. Distributed adaptive neural network control applied to a formation tracking of a group of low-cost underwater drones in hazardous environments[J]. Applied sciences, 2020, 10(5): 1732.
[151] RYUH Youngsun, YANG Gihun, LIU Jindong, et al. A school of robotic fish for mariculture monitoring in the sea coast[J]. Journal ofbionic engineering, 2015, 12(1): 37–46.
[152] MEDAGODA L, WILLIAMS S B, PIZARRO O, et al. Mid-water current aided localization for autonomous underwater vehicles[J]. Autonomous robots, 2016, 40(7): 1207–1227.
[153] AN Ruochen, GUO Shuxiang, YU Yuanhua, et al. Task planning and collaboration of jellyfish-inspired multiple spherical underwater robots[J]. Journal of bionic engineering, 2022, 19(3): 643–656.
[154] BOTELHO S, NEVES R, TADDEI L, et al. Using augmented state Kalman filter to localize multi autonomous underwater vehicles[J]. Journal of the brazilian computer society, 2007, 13(2): 61–70.
[155] HE Yanlin, ZHU Lianqiang, SUN Guangkai, et al. Cooperative localization and evaluation of small- scaled spherical underwater robots[J]. Microsystem technologies, 2019, 25(2): 573–585.
[156] WANG Sen, CHEN Ling, HU Huosheng, et al. Underwater localization and environment mapping using wireless robots[J]. Wireless personal communications, 2013, 70(3): 1147–1170.
[157] HARBIN J, GERASIMOU S, MATRAGKAS N, et al. Model-driven simulation-based analysis for multi-robot systems[C]//2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems. Fukuoka: IEEE, 2021: 331?341.
[158] PAULL L, SETO M, LEONARD J J. Decentralized cooperative trajectory estimation for autonomous underwater vehicles[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago: IEEE, 2014: 184?191.
[159] CUI Ronxin, CHEN Lepeng, YANG Chengguang, et al. Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities[J]. IEEE transactions on industrial electronics, 2017, 64(8): 6785–6795.
[160] HALSTED T, SCHWAGER M. Distributed multi- robot localization from acoustic pulses using Euclid- ean distance geometry[C]//2017 International Symposium on Multi-Robot and Multi-Agent Systems. Los Angeles: IEEE, 2017: 104?111.
[161] GUO Shuxiang, CAO Sheng, GUO Jian, et al. Study on distributed data processing system for decentralized spherical multi-robot based on edge computing and blockchain[C]//2020 IEEE International Conference on Mechatronics and Automation. Beijing: IEEE, 2020: 1852?1857.
[162] LIU Hao, WANG Yanhu, LEWIS F L. Robust distributed formation controller design for a group of unmanned underwater vehicles[J]. IEEE transactions on systems, man, and cybernetics:systems, 2021, 51(2): 1215–1223.
[163] RATHNAM, BIRK A. Distributed communicative exploration under underwater communication constraints[C]//2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. Kyoto: IEEE, 2011: 339?344.
[164] HOLLINGER G A, YERRAMALLI S, SINGH S, et al. Distributed coordination and data fusion for underwater search[C]//2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011: 349? 355.
[165] JIA Qingyong, XU Hongli, FENG Xisheng, et al. Research on cooperative area search of multiple underwater robots based on the prediction of initial target information[J]. Ocean engineering, 2019, 172: 660–670.
[166] PENG Zhouhua, WANG Dan, WANG Hao, et al. Distributed coordinated tracking of multiple autonomous underwater vehicles[J]. Nonlinear dynamics, 2014, 78(2): 1261–1276.
[167] SUN Bing, ZHU Daqi, TIAN Chen, et al. Complete coverage autonomous underwater vehicles path planning based on glasius bioinspired neural network algorithm for discrete and centralized programming[J]. IEEE transactions on cognitive and developmental systems, 2019, 11(1): 73–84.
[168] GU Shuoxin, ZHANG Linshuai, GUO Shuxiang, et al. Communication and cooperation for spherical underwater robots by using acoustic transmission[J]. IEEE/ ASME transactions on mechatronics, 2023, 28(1): 292–301.
[169] JIA Qingyong, XU Hongli, LI Guannan, et al. Research on synergy pursuit strategy of multiple underwater robots[J]. Journal of intelligent & robotic systems, 2020, 97(3-4): 673–694.
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