无人集群系统协同运动规划技术综述

doi:10.12382/bgxb.2022.0930

摘要/Abstract

摘要：

地面无人集群由多个地面无人移动平台构成,能够通过各无人平台间相互协同完成统一的系统协同目标,在军事和交通系统等领域能够发挥重要的作用。协同运动规划作为无人集群系统协同的关键技术之一,近年来在理论和应用等方面的研究受到越来越多的关注。针对此研究问题,归纳总结了近年来相关领域的无人集群系统协同运动规划的研究成果,阐述了无人集群协同运动规划技术的研究背景和意义,结合国内外发展现状和研究进展,对多车协同系统的应用进行了表述,并根据主流研究方法使用的框架和算法,对现有的协同运动规划技术进行分类,并讨论了各类方法的主要特点,同时对相关代表性工作进行讨论,提出了无人集群协同规划技术面临的挑战和未来发展方向。

关键词: 无人车辆, 多车协同, 运动规划, 多智能体系统协同

Abstract:

An unmanned ground swarm system consists of multiple unmanned ground mobile platforms, which can achieve common objectives through cooperation and has promising applications in military and transportation systems. Cooperative motion planning is one of the key technologies in the cooperation of unmanned swarm systems or vehicles. It has received increasing attention in both theoretical and application research. This review summarizes and analyzes recent advances in cooperative motion planning of unmanned swarm systems, and provides the background of relevant research. Then the techniques utilized in cooperative motion planning and its applications are briefly discussed considering its development in China and beyond. These techniques are categorized according to different frameworks and algorithms. With such a classification, representative works are discussed regarding their features. Moreover, the challenges and future development of cooperative motion planning are proposed.

Key words: unmanned vehicles, cooperation of multiple vehicles, motion planning, cooperation of multi-agent systems

中图分类号:

TJ301

孔国杰, 冯时, 于会龙, 巨志扬, 龚建伟. 无人集群系统协同运动规划技术综述[J]. 兵工学报, 2023, 44(1): 11-26.

KONG Guojie, FENG Shi, YU Huilong, JU Zhiyang, GONG Jianwei. A Review on Cooperative Motion Planning of Unmanned Vehicles[J]. Acta Armamentarii, 2023, 44(1): 11-26.

图/表 8

图1 多车协同系统应用场景

Fig.1 Applications of cooperative motion planning

图2 国内外多车协同系统研究现状

Fig.2 Research status of cooperative multi-vehicle systems

图3 多车协同规划算法框架

Fig.3 Framework of multi-vehicle path planning

图4 CBS算法示意图[74]

Fig.4 Illustration of CBS algorithm[74]

图5 基于预构建碰撞检测法的多车协同规划算法[79]

Fig.5 Multi-vehicle cooperative planning algorithms based on pre-constructed collision detection[79]

图6 基于优化的分布式多车协同规划算法

Fig.6 Distributed multi-vehicle cooperative planning based on optimization

图7 速度障碍法[120]

Fig.7 Velocity obstacle-based methods[120]

图8 基于博弈论的多车协同规划方法[127]

Fig.8 Game theory-based multi-vehicle cooperative planning

参考文献 152

[1]	RIZK Y, AWAD M, TUNSTEL E. Cooperative heterogeneous multi-robot systems: a survey[J]. ACM Computing Surveys, 2019, 52(2): 1-31.
[2]	陈慧岩, 张玉. 军用地面无人机动平台技术发展综述[J]. 兵工学报, 2014, 35(10): 1696-1706. doi: 10.3969/j.issn.1000-1093.2014.10.026
	CHEN H Y, ZHANG Y. An overview of research on military unmanned ground vehicles[J]. Acta Armamentarii, 2014, 35(10): 1696-1706. (in Chinese)
[3]	AHMED N, CORTES J, MARTINEZ S. Distributed control and estimation of robotic vehicle networks: overview of the special issue[J]. IEEE Control Systems, 2016, 36(2): 36-40. doi: 10.1109/MCS.2015.2512030 URL
[4]	ZHAI Z, MARTÍNEZ ORTEGA J, LUCAS MARTÍNEZ N, et al. A mission planning approach for precision farming systems based on multi-objective optimization[J]. Sensors, 2018, 18(6):1795. doi: 10.3390/s18061795 URL
[5]	QIAN D W, XI Y F. Leader-follower formation maneuvers for multi-robot systems via derivative and integral terminal sliding mode[J]. Applied sciences, 2018, 8(7):1045.
[6]	SCHULZE M, NÖCKER G, BOHM K. PReVENT: a european program to improve active safety[C]// Proceedings of the 5th International Conference on Intelligent Transportation Systems Telecommunications. Brest, France:[s.n.], 2015.
[7]	XU C, QIN H M, WANG J Q. Simply analysis of connected vehicle’s role in a partially-connected vehicle group[C]//Proceedings of 2014 International Symposium on Instrumentation and Measurement, Sensor Network and Automation. Ottawa, Canada: IEEE, 2014, 4: 1253-1256.
[8]	MIGLANI A, KUMAR N. Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges[J]. Vehicular Communications, 2019, 20: 100184. doi: 10.1016/j.vehcom.2019.100184 URL
[9]	TIAN Y, ZHANG K L, LI J Y, et al. LSTM-based traffic flow prediction with missing data[J]. Neurocomputing, 2018, 318: 297-305. doi: 10.1016/j.neucom.2018.08.067 URL
[10]	OTTE M, KUHLMAN M J, SOFGE D. Auctions for multi-robot task allocation in communication limited environments[J]. Autonomous Robots, 2020, 44(3): 547-584. doi: 10.1007/s10514-019-09828-5 URL
[11]	WANG D, ZHANG W, SONG B, et al. Market-based model in CR-IoT: a Q-probabilistic multi-agent reinforcement learning approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 6(1): 179-188. doi: 10.1109/TCCN.2019.2950242 URL
[12]	QU G N, BROWN D, LI N. Distributed greedy algorithm for multi-agent task assignment problem with submodular utility functions[J]. Automatica, 2019, 105: 206-215. doi: 10.1016/j.automatica.2019.03.007 URL
[13]	FUJII S, FUJITA A, UMEDU T, et al. Cooperative vehicle positioning via V2V communications and onboard sensors[C]// Proceedings of 2011 IEEE Vehicular Technology Conference. San Francisco, CA, US:IEEE, 2011: 1-5.
[14]	HOSSAIN M, ELSHAFIEY I, AL-SANIE A. Cooperative vehicle positioning with multi-sensor data fusion and vehicular communications[J]. Wireless Networks, 2019, 25(3): 1403-1413. doi: 10.1007/s11276-018-1772-6 URL
[15]	KUBE C R, ZHANG H. The use of perceptual cues in multi-robot box-pushing[C]// Proceedings of IEEE international conference on robotics and automation. Minneaplis, MN, US:IEEE, 1996, 3: 2085-2090.
[16]	TOMIZUKA M. Advanced vehicle control systems (AVCS) research for automated highway systems in California PATH[C]//Proceedings of VNIS’94-1994 Vehicle Navigation and Information Systems Conference. Yokohama, Japan:IEEE, 1994: PLEN41-PLEN45.
[17]	GERDTS M, HENRION R, HÖMBERG D, et al. Path planning and collision avoidance for robots[J]. Numerical Algebra, Control & Optimization, 2012, 2(3): 437-463.
[18]	GABRIELA S, IRINA-CARMEN A. Automated conflict resolution in air traffic management[J]. INCAS Bulletin, 2017, 9(1):91-104.
[19]	ZHU H, ALONSO-MORA J. Chance-constrained collision avoidance for mavs in dynamic environments[J]. IEEE Robotics and Automation Letters, 2019, 4(2): 776-783. doi: 10.1109/LRA.2019.2893494
[20]	NGUYEN L A, HARMAN T L, FAIRCHILD C. Swarmathon: a swarm robotics experiment for future space exploration[C]// Proceedings of 2019 IEEE International Symposium on Measurement and Control in Robotics. Houston, TX, US:IEEE, 2019: B1-3-1-B1-3-4.
[21]	FIORINI P, SHILLER Z. Motion planning in dynamic environments using velocity obstacles[J]. The international journal of robotics research, 1998, 17(7): 760-772. doi: 10.1177/027836499801700706 URL
[22]	JENIE Y I, KAMPEN E J, DE VISSER C C, et al. Selective velocity obstacle method for deconflicting maneuvers applied to unmanned aerial vehicles[J]. Journal of Guidance, Control, and Dynamics, 2015, 38(6): 1140-1146. doi: 10.2514/1.G000737 URL
[23]	BORENSTEIN J, KOREN Y. Real-time obstacle avoidance for fast mobile robots[J]. IEEE Transactions on systems, Man, and Cybernetics, 1989, 19(5): 1179-1187. doi: 10.1109/21.44033 URL
[24]	COSÍO F A, CASTANEDA M A P. Autonomous robot navigation using adaptive potential fields[J]. Mathematical and computer modelling, 2004, 40(9/10): 1141-1156. doi: 10.1016/j.mcm.2004.05.001 URL
[25]	陈山枝, 胡金玲, 时岩, 等. LTE-V2X车联网技术、标准与应用[J]. 电信科学, 2018, 34(4):1-11. doi: 10.11959/j.issn.1000-0801.2018140
	CHEN S Z, HU J L, SHI Y, et al. Technologies,standards and applications of LTE-V2X for vehicular networks[J]. Telecommunications Science, 2018, 34(4): 1-11. (in Chinese) doi: 10.11959/j.issn.1000-0801.2018140
[26]	缪立新, 王发平. V2X车联网关键技术研究及应用综述[J]. 汽车工程学报, 2020, 10(1):1-12.
	MIU L X, WANG F P. Review on research and applications of v2x key technologies[J]. Chinese Journal of Automotive Engineering, 2020, 10(1):1-12. (in Chinese)
[27]	HHHA B, MHR C, HAE A, et al. Multi V2X channels resource allocation algorithms for D2D 5G network performance enhancement[J]. Vehicular Communications, 2021, 31: 100371. doi: 10.1016/j.vehcom.2021.100371 URL
[28]	XIONG F, LI A J, WANG H, et al. An SDN-MQTT based communication system for battlefield UAV swarms[J]. IEEE Communications Magazine, 2019, 57(8): 41-47. doi: 10.1109/MCOM.2019.1900291
[29]	王祝. 多无人机协同规划控制的关键技术研究[D]. 北京: 北京理工大学, 2017.
	WANG Z. Research on Key Technologies of Multi-UAV Cooperative Planning and Control[D]. Beijing: Beijing Institute of Technology, 2017. (in Chinese)
[30]	AFSARI K, GUPTA S, AFKHAMIAGHDA M, et al. Applications of collaborative industrial robots in building construction[C]//54th ASC Annual International Conference Proceedings. Minneapolis, MN, US: Associated School of Construction, 2018: 472-479.
[31]	YANG Z, HUANG H, WANG G, et al. Cooperative driving model for non-signalized intersections with cooperative games[J]. Journal of Central South University, 2018, 25(9): 2164-2181. doi: 10.1007/s11771-018-3905-6 URL
[32]	ZHAO X, WANG J, CHEN Y, et al. Multi-objective cooperative scheduling of CAVs at non-signalized intersection[C]// Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems. Maui, HI, US:IEEE, 2018: 3314-3319.
[33]	申剑峰. 车车协同下无人车换道的过程决策和轨迹规划[D]. 合肥: 合肥工业大学, 2018.
	SHEN J F. Decision making and trajectory planning in lane-changing process of automated vehicle with vehicle-to-vehicle collaboration[D]. Hefei: Hefei University of Technology, 2018. (in Chinese)
[34]	张立雄, 郭艳, 李宁, 等. 基于多智能体强化学习的无人车分布式路径规划方法[J]. 电声技术, 2021, 45(3): 52-57.
	ZHANG L X, GUO Y, LI N, et al. Path planning method of autonomous vehicles based on multi-agent reinforcement learning[J]. Audio Engineering, 2021, 45(3): 52-57. (in Chinese)
[35]	常彦文. 多智能车协同编队与运动规划方法的研究[D]. 北京: 北京石油化工学院, 2022.
	CHANG Y. Research on collaborative formation and motion planning method of multi-intelligent vehicles[D]. Beijing: Beijing Institute of Petrochemical Technology, 2022. (in Chinese)
[36]	宋文静, 李为民, 肖金科, 等. 基于MAS的区域反导发射车协同拦截规划研究[J]. 现代防御技术, 2015, 43(6): 81-86, 123.
	SONG W J, LI W M, XIAO J K, et al. Cooperative interception planning of launcher vehilces based on mas in theater antimissile system[J]. Modern Defence Technology, 2015, 43(6): 81-86, 123. (in Chinese)
[37]	KONOLIDGE K, NILSSON N J. Multi-agent planning systems[C]// Proceedings of First National Conference on Artifical Intelligence. Menlo Park, CA, US:AAAI, 1980: 138-142.
[38]	毛昱天, 陈杰, 方浩, 等. 连通性保持下的多机器人系统分布式群集控制[J]. 控制理论与应用, 2014, 31(10): 1393-1403.
	MAO Y T, CHENG J, FANG H, et al. Decentralized flocking of multi-robot systems with connectivity maintaince. Contol Theory & Applications, 2014, 31(10): 1393-1403. (in Chinese)
[39]	郄天琪, 王伟达, 杨超, 等. 基于模型预测控制方法的智能车路径规划策略研究[C]// 2021中国汽车工程学会年会论文集(1).北京:机械工业出版社, 2021:119-123.
	QIE T Q, WANG W D, YANG C, et al. A path planning method for intelligent vehicles based on model predictive control method[C]// Proceedings of China-SAE Congress(1). Beijing:China Machine Press, 2021: 119-123. (in Chinese)
[40]	李威. 模型预测控制在轨迹规划和车辆控制中的应用研究[D]. 杭州: 浙江大学, 2021.
	LI W. Application of model predictive cotrol in trajectory planning and vehicle control[D]. Hangzhou: Zhejiang University, 2021. (in Chinese)
[41]	杨博, 张缓缓, 江忠顺. 基于模型预测控制的车辆避障路径跟踪控制仿真研究[J]. 智能计算机与应用, 2020, 10(12): 99-103.
	YANG B, ZHANG H H, JIANG Z S. Simulation of vehicle obstacle avoidance path tracking control based on model predictive control[J]. Intelligent Computer and Applications, 2020, 10(12): 99-103. (in Chinese)
[42]	冀杰, 唐志荣, 吴明阳, 等. 面向车道变换的路径规划及模型预测轨迹跟踪[J]. 中国公路学报, 2018, 31(4): 172-179.
	JI J, TANG Z R, WU M Y, et al. Path planning and tracking for lane changing based on model predictive cotnrol[J]. China Journal of Highway and Transport, 2018, 31(4): 172-179. (in Chinese)
[43]	詹璟原. 多智能体系统预测协同控制研究[D]. 上海: 复旦大学, 2013.
	ZHAN J Y. Cooperative predictive control of multi-agent systems[D]. Shanghai: Fudan University, 2013. (in Chinese)
[44]	李立, 徐志刚, 赵祥模, 等. 智能网联汽车运动规划方法研究综述[J]. 中国公路学报, 2019, 32(6): 20-33. doi: 10.19721/j.cnki.1001-7372.2019.06.002
	LI L, XU Z G, ZHAO X M, et al. Review of motion planning methods of intelligent connected vehicles[J]. China Journal of Highway and Transport, 2019, 32(6): 20-33. (in Chinese) doi: 10.19721/j.cnki.1001-7372.2019.06.002
[45]	采国顺, 刘昊吉, 冯吉伟, 等. 智能汽车的运动规划与控制研究综述[J]. 汽车安全与节能学报, 2021, 12(3): 279-297.
	CAI G S, LIU H J, FENG J W, et al. Review on the research of motion planning and control for intelligent vehicles[J]. Journal of Automotive Safety and Energy, 2021, 12(3): 279-297. (in Chinese)
[46]	刘阳. 基于博弈论的车辆队列运动协同分层控制算法研究[D]. 长春: 吉林大学, 2020.
	LIU Y. Research on hierarchical control algorithm of motion cooperation for vehicle platonn based on game theory[D]. Changchun: Jilin University, 2020. (in Chinese)
[47]	李庆华, 王佳慧, 李海明, 等. 一种双阶段多智能体路径规划算法[J]. 科学技术与工程, 2021, 21(22): 9425-9431.
	LI Q H, WANG J H, LI H M, et al. A two-stage multi-agent path planning algorithm[J]. Science Technology and Engineering, 2021, 21(22): 9425-9431. (in Chinese)
[48]	雷小宇, 杨胜跃, 张亚鸣, 等. 基于协同进化的多智能体机器人路径规划[J]. 计算机系统应用, 2010, 19(11): 157-161.
	LEI X Y, YANG S Y, ZHANG Y M, et al. Path planning research for multi-agent robot based on co-evolution[J]. Computer Systems & Applications, 2010, 19(11): 157-161. (in Chinese)
[49]	王超, 赵晓哲, 康晓予. 面向编队协同防空决策的多智能体规划方法[J]. 舰船电子工程, 2009, 29(1): 62-64.
	WANG C, ZHAO X Z, KANG X Y. A Multi-agent planning method oriented formation cooperative anti-air decision[J]. Ship Electronic Engineering, 2009, 29(1): 62-64. (in Chinese)
[50]	张思宇. 多无人机协同航迹规划及其控制方法研究[D]. 北京: 北京理工大学, 2016.
	ZHANG S Y. Research on Multi-UAVs Cooperative Trajectory Planning and Control Method[D]. Beijing: Beijing Institute of Technolog, 2016. (in Chinese)
[51]	付梦印, 杨毅, 岳裕丰, 等. 地空协同无人系统综述[J]. 国防科技, 2021, 42(3): 1-8.
	FU M Y, YANG Y, YUE Y F, et al. A review of ground-air cooperative unmanned system[J]. National Defense Technology, 2021, 42(3): 1-8. (in Chinese)
[52]	孟红, 朱森. 地面无人系统的发展及未来趋势[J]. 兵工学报, 2014, 35(增刊1): 1-7.
	MENG H, ZHU S. The development and future trends of unmanned ground systems[J]. Acta Armamentarii, 2014, 35(S1): 1-7. (in Chinese)
[53]	王荣浩, 邢建春, 王平, 等. 地面无人系统的多智能体协同控制研究综述[J]. 动力学与控制学报, 2016, 14(2): 97-108.
	WANG R H, XING J C, WANG P, et al. An overview on multi-agents cooperative control of umanned ground systems[J]. Journal of Dynamics and Control, 2016, 14(2): 97-108. (in Chinese) doi: 10.1016/0165-1889(90)90008-5 URL
[54]	LI B, ZHANG Y M, SHAO Z J, et al. Simultaneous versus joint computing: A case study of multi-vehicle parking motion planning[J]. Journal of Computational Science, 2017, 20: 30-40. doi: 10.1016/j.jocs.2017.03.015 URL
[55]	袁利平, 夏洁, 陈宗基. 多无人机协同路径规划研究综述[J]. 飞行力学, 2009, 27(5): 1-5, 10.
	YUAN L P, XIA J, CHEN Z J. Survey of cooperative path planning for multiple uavs[J]. Flight Dynamics, 2009, 27(5): 1-5, 10. (in Chinese)
[56]	赵津, 张博, 潘霞, 等. 车联网通信技术及应用前景研究[J]. 时代汽车, 2021(6): 15-16, 32.
	ZHAO J, ZHANG B, PAN X, et al. Research on communications and application of vehicular networks[J]. Auto Time, 2021(6): 15-16, 32. (in Chinese)
[57]	吴志安, 赖永朋, 朱有亮, 等. 基于协同合作的多智能体控制系统算法探究[J]. 机电工程技术, 2022, 51(8): 82-86.
	WU Z A, LAI Y M, ZHU Y L, et al. Research on multi-agent control system algorithm based on cooperative cooperation[J]. Mechanical & Electrical Engineering Technology, 2022, 51(8): 82-86. (in Chinese)
[58]	SHARON G, STERN R, FELNER A, et al. Conflict-based search for optimal multi-agent pathfinding[J]. Artificial Intelligence, 2015, 219: 40-66. doi: 10.1016/j.artint.2014.11.006 URL
[59]	MIRHELI A, HAJIBABAI L, HAJBABAIE A. Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 412-425. doi: 10.1016/j.trc.2018.04.026 URL
[60]	杨晨, 张少卿, 孟光磊. 多无人机协同任务规划研究[J]. 指挥与控制学报, 2018, 4(3): 234-248.
	YANG C, ZHANG S Q, MENG G L. Multi-UAV cooperative mission planning[J]. Journal of Command and Control, 2018, 4(3): 234-248. (in Chinese)
[61]	ZHANG X, GUAN X, HWANG I. et al. A hybrid distributed-centralized conflict resolution approach for multi-aircraft based on cooperative co-evolutionary[J]. Science China Information Sciences, 2013, 56: 1-16.
[62]	RASHEED A A A, ABDULLAH M N, Al-ARAJI A S. A review of multi-agent mobile robot systems applications[J]. International Journal of Electrical & Computer Engineering, 2022, 12(4): 3517-3529.
[63]	STANDLEY, TREVOR. Finding optimal solutions to cooperative pathfinding problems[C]// Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. Atlanta, GE, US:AAAI, 2010, 24(1):173-178.
[64]	刘庆周, 吴锋. 多智能体路径规划研究进展[J]. 计算机工程, 2020, 46(4):1-10.
	LIU Q Z, WU F. Research Progress of Multi-Agent Path Planning[J]. Computer Engineering, 2020, 46(4): 1-10. (in Chinese)
[65]	郭超, 陈香玲, 郭鹏, 等. 基于时空A*算法的多AGV无冲突路径规划[J]. 计算机系统应用, 2022, 31(4): 360-368.
	GUO C, CHEN X L, GUO P, et al. Multi-AGV non-conflict path planning based on space-time A* algorithm[J]. Computer Systems & Applications, 2022, 31(4): 360-368. (in Chinese)
[66]	刘丹, 侯山鹏, 曾垂国. 动态环境下多智能体的路径规划[J]. 电脑知识与技术, 2010, 6(12): 3022-3024.
	LIU D, HOU S P, ZENG C G. Multi-agent path planning in the dynamic environment[J]. Computer Knowledge and Technology, 2010, 6(12): 3022-3024. (in Chinese)
[67]	刘志飞, 曹雷, 赖俊, 等. 多智能体路径规划综述[J]. 计算机工程与应用, 2022, 58(20): 43-62. doi: 10.3778/j.issn.1002-8331.2203-0467
	LIU Z F, CAO L, LAI J, et al. Overview of Multi-Agent Path Finding. Computer Engineering and Applications, 2022, 58(20): 43-62. (in Chinese) doi: 10.3778/j.issn.1002-8331.2203-0467
[68]	SHARON G, STERN R, FELNER A, et al. Meta-agent conflict-based search for optimal multi-agent path finding[C]//Proceedings of International symposium on combinatorial search. Niagara Falls, Ontario, Canada: AAAI, 2012, 3(1):97-104.
[69]	BOYARSKI E, FELNER A, STERN R, et al. ICBS: Improved conflict-based search algorithm for multi-agent pathfinding[C]// Proceedings of th Twenty-Fourth International Joint Conference on Artificial Intelligence. Buenos, Aires, Argentina:AAAI, 2015: 740-746.
[70]	BARER M, SHARON G, STERN R, et al. Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem[C]//Proceedings of the Twenty-first Europeah Conference on Artificial Intelligence. Prague, Czech Republic: IOS Press, 2014:961-962.
[71]	乔乔, 王艳, 纪志成. 基于冲突搜索算法的多机器人路径规划[J]. 系统仿真学报, 2022, 34(12): 2659-2669. doi: 10.16182/j.issn1004731x.joss.22-FZ0926
	QIAO Q, WANG Y, JI C W. Multi-robot path planning based on cbs algorithm[J]. Journal of System Simulation, 2022, 34(12): 2659-2669. (in Chinese) doi: 10.16182/j.issn1004731x.joss.22-FZ0926
[72]	王东, 于连波, 曹品钊, 等. 基于冲突分类与消解的多智能体路径规划算法设计[J]. 导航定位与授时, 2022, 9(5): 56-66.
	WANG D, YU L B, CAO P Z, et al. Design of a multi-agent path planning algorithm based on conflict classification and resolution[J]. Navigation Positioning and Timing, 2022, 9(5): 56-66. (in Chinese)
[73]	于连波, 曹品钊, 石亮, 等. 基于改进冲突搜索的多智能体路径规划算法[J/OL]. 航空学报, 2022(2022-09-22). https://hkxb.buaa.edu.cn/CN/10.7527/S1000-6893.2022.27648.
	YU L B, CAO P Z, SHI L, et al. An improved conflict-based search algorithm for multi-agent path planning[J/OL]. Acta Aeronautica et Astranautica Sinica, 2022(2022-09-22): https://hkxb.buaa.edu.cn/CN/10.7527/S1000-6893.2022.27648. (in Chinese)
[74]	张峰, 李明强, 唐思琦, 等. 多智能体协同决策方法研究[J]. 中国电子科学研究院学报, 2022, 17(9): 905-910.
	ZHANG F, LI M Q, TANG S Q, et al. Research on multi-agent cooperative decision-making method[J]. Journal of China Academy of Electronics and Information Technology, 2022, 17(9): 905-910. (in Chinese)
[75]	MIRHELI A, TAJALLI M, HAJIBABAI L, et al. A consensus-based distributed trajectory control in a signal-free intersection[J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 161-176. doi: 10.1016/j.trc.2019.01.004 URL
[76]	COHEN L, URAS T, KOENIG S. Feasibility study: using highways for bounded-suboptimal multi-agent path finding[C]//Proceedings of the Eighth Annual Symposium on Combinatorial Search. Ein Gedi, the Dead Sea, Israel: AAAI, 2015: 2-8.
[77]	COHEN L, URAS T, KUMAR T K S, et al. Improved solvers for bounded-suboptimal multi-agent path finding[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. New York, NY, US: IJCAI/AAAI Press, 2016: 3067-3074.
[78]	ESTERLE K, KESSLER T, KNOLL A. Optimal behavior planning for autonomous driving: a generic mixed-integer formulation[C]//Proceedings of 2020 IEEE Intelligent Vehicles Symposium (IV). Las Vegas, NV, US: IEEE, 2020: 1914-1921.
[79]	KESSLER T, KNOLL A. Cooperative multi-vehicle behavior coordination for autonomous driving[C]//Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France: IEEE, 2019: 1953-1960.
[80]	XU H L, ZHANG Y, CASSANDRAS C G, et al. A bi-level cooperative driving strategy allowing lane changes[J]. Transportation Research Part C: Emerging Technologies, 2020, 120: 102773. doi: 10.1016/j.trc.2020.102773 URL
[81]	PEI H X, FENG S, ZHANG Y, et al. A cooperative driving strategy for merging at on-ramps based on dynamic programming[J]. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11646-11656. doi: 10.1109/TVT.2019.2947192 URL
[82]	MIRHELI A, TAJALLI M, HAJIBABAI L, et al. A consensus-based distributed trajectory control in a signal-free intersection[J]. Transportation Research part C: Emerging Technologies, 2019, 100: 161-176. doi: 10.1016/j.trc.2019.01.004 URL
[83]	MIRHELI A, HAJIBABAI L, HAJBABAIE A. Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 412-425. doi: 10.1016/j.trc.2018.04.026 URL
[84]	CHOI H L, KIM K S, JOHNSON L B, et al. Potential game-theoretic analysis of a market-based decentralized task allocation algorithm[M] //CHONGN Y, CHOY J. Distributed Autonomous Robotic Systems. Tokyo, Japan: Springer, 2016: 207-220.
[85]	YU J, LAVALLE S M. Multi-agent path planning and network flow[J]. Springer Tracts in Advanced Robotics, 2013, 86: 157-173.
[86]	LIENKE C, WISSING C, KELLER M, et al. Predictive driving: fusing prediction and planning for automated highway driving[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(3): 456-467. doi: 10.1109/TIV.2019.2919477 URL
[87]	PIERSON A, SCHWARTING W, KARAMAN S, et al. Learning risk level set parameters from data sets for safer driving[C]//Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France: IEEE, 2019: 273-280.
[88]	HANG P, LV C, HUANG C, et al. An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14458-14469. doi: 10.1109/TVT.2020.3040398 URL
[89]	LI X P, XUE Q F, ZHAO J W, et al. Causal reasoning in multi-object interaction on the traffic scene: occlusion-aware prediction of visibility fluent[J]. IEEE Access, 2020, 8: 80527-80535. doi: 10.1109/ACCESS.2020.2988677 URL
[90]	YI Z W, LI L H, QU X, et al. Using artificial potential field theory for a cooperative control model in a connected and automated vehicles environment[J]. Transportation Research Record, 2020, 2674(9): 1005-1018. doi: 10.1177/0361198120933271 URL
[91]	TAHMASBI-SARVESTANI A, MAHJOUB H N, FALLAH Y P, et al. Implementation and evaluation of a cooperative vehicle-to-pedestrian safety application[J]. IEEE Intelligent Transportation Systems Magazine, 2017, 9(4): 62-75. doi: 10.1109/MITS.2017.2743201 URL
[92]	宿浩, 张宝琳, 籍艳, 等. 基于虚拟排斥力的移动多智能体覆盖控制动态博弈算法[J]. 中国科学:信息科学, 2022, 52(12): 2195-2212.
	SU H, ZHANG B L, JI Y, et al. Dynamic game coverage control algorithms for multiple mobile agents through virtual repulsive force[J]. Scientia Sinica(Informationis), 2022, 52(12): 2195-2212. (in Chinese)
[93]	CHOI D, CHHABRA A, KIM D. Intelligent cooperative collision avoidance via fuzzy potential fields[J]. Robotica, 2022, 40(6): 1919-1938. doi: 10.1017/S0263574721001454 URL
[94]	LIU W, WENG Z Y, CHONG Z J, et al. Autonomous vehicle planning system design under perception limitation in pedestrian environment[C]//Proceedings of 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics. Siem Reap, Cambodia: IEEE, 2015: 159-166.
[95]	KLISCHAT M, ALTHOFF M. A multi-step approach to accelerate the computation of reachable sets for road vehicles[C]//Proceedings of 2020 IEEE 23rd International Conference on Intelligent Transportation Systems. Rhodes, Greece: IEEE, 2020: 1-7.
[96]	ZHAI C J, LIU Y G, LUO F. A switched control strategy of heterogeneous vehicle platoon for multiple objectives with state constraints[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(5): 1883-1896. doi: 10.1109/TITS.2018.2841980 URL
[97]	LIU C, LIN C Y, TOMIZUKA M. The convex feasible set algorithm for real time optimization in motion planning[J]. SIAM Journal on Control and optimization, 2018, 56(4): 2712-2733. doi: 10.1137/16M1091460 URL
[98]	WEI C F, ROMANO R, MERAT N, et al. Risk-based autonomous vehicle motion control with considering human driver’s behaviour[J]. Transportation Research Part C: Emerging Technologies, 2019, 107: 1-14. doi: 10.1016/j.trc.2019.08.003 URL
[99]	MANZINGER S, ALTHOFF M. Tactical decision making for cooperative vehicles using reachable sets[C]// Proceedings of 2018 21st International Conference on Intelligent Transportation Systems. Maui, HI, US:IEEE, 2018: 444-451.
[100]	MANZINGER S, PEK C, ALTHOFF M. Using reachable sets for trajectory planning of automated vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2021, 6(2): 232-248. doi: 10.1109/TIV.2020.3017342 URL
[101]	SÖNTGES S, ALTHOFF M. Computing the drivable area of autonomous road vehicles in dynamic road scenes[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(6): 1855-1866. doi: 10.1109/TITS.2017.2742141 URL
[102]	ZHOU H Y, LIU C L. Distributed motion coordination using convex feasible set based model predictive control[C]// Proceedings of 2021 IEEE International Conference on Robotics and Automation. Xi’an, China:IEEE, 2021: 8330-8336.
[103]	HARTMANN M, WATZENIG D. Optimal motion planning with reachable sets of vulnerable road users[C]//Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France, IEEE, 2019: 891-898.
[104]	BRESSON R, SARAYDARYAN J, DUGDALE J, et al. Socially compliant navigation in dense crowds[C]//Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France: IEEE, 2019: 64-69.
[105]	DING W C, ZHANG L, CHEN J, et al. Safe trajectory generation for complex urban environments using spatio-temporal semantic corridor[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 2997-3004. doi: 10.1109/LRA.2019.2923954 URL
[106]	龚建伟, 姜岩, 徐威. 无人驾驶车辆模型预测控制[M]. 北京: 北京理工大学出版社, 2014.
	GONG J W, JIANG Y, XU W. Model Predictive Control for Self-driving Vehicles[M]. Beijing: Beijing Institute of Technology Press, 2014. (in Chinese)
[107]	ZHOU Y, CHUNG E, BHASKAR A, et al. A state-constrained optimal control based trajectory planning strategy for cooperative freeway mainline facilitating and on-ramp merging maneuvers under congested traffic[J]. Transportation Research Part C: Emerging Technologies, 2019, 109: 321-342. doi: 10.1016/j.trc.2019.10.017 URL
[108]	CAO W J, MUKAI M, KAWABE T, et al. Cooperative vehicle path generation during merging using model predictive control with real-time optimization[J]. Control Engineering Practice, 2015, 34: 98-105. doi: 10.1016/j.conengprac.2014.10.005 URL
[109]	RIOS-TORRES J, MALIKOPOULOS A A. Automated and cooperative vehicle merging at highway on-ramps[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(4): 780-789. doi: 10.1109/TITS.2016.2587582 URL
[110]	YAN Y J, WANG J X, ZHANG K R, et al. Path planning using a kinematic driver-vehicle-road model with consideration of driver’s characteristics[C]// Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France:IEEE, 2019: 2259-2264.
[111]	THEERTHALA R R, SAI BHARGAV KUMAR A V S, BABU M, et al. Motion planning framework for autonomous vehicles: atime scaled collision cone interleaved model predictive control approach[C]// Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France:IEEE, 2019: 1075-1080.
[112]	JAIN V, KOLBE U, BREUEL G, et al. Reacting to multi-obstacle emergency scenarios using linear time varying model predictive control[C]// Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France:IEEE, 2019: 1822-1829.
[113]	VAN NUNEN E, REINDERS J, SEMSAR-KAZEROONI E, et al. String stable model predictive cooperative adaptive cruise control for heterogeneous platoons[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(2): 186-196. doi: 10.1109/TIV.2019.2904418 URL
[114]	ZHAI C J, LIU Y G, LUO F. A switched control strategy of heterogeneous vehicle platoon for multiple objectives with state constraints[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(5): 1883-1896. doi: 10.1109/TITS.2018.2841980 URL
[115]	XU L W, ZHUANG W C, YIN G D, et al. Energy-oriented cruising strategy design of vehicle platoon considering communication delay and disturbance[J]. Transportation Research Part C: Emerging Technologies, 2019, 107: 34-53. doi: 10.1016/j.trc.2019.07.019 URL
[116]	EARL M G, D’ANDREA R. A decomposition approach to multi-vehicle cooperative control[J]. Robotics and Autonomous Systems, 2007, 55(4): 276-291. doi: 10.1016/j.robot.2006.11.002 URL
[117]	李樾, 韩维, 陈清阳, 等. 基于改进的速度障碍法的有人/无人机协同系统三维实时避障方法[J]. 西北工业大学学报, 2020, 38(2): 309-318.
	LI Y, HAN W, CHEN Q Y, et al. Real-time obstacle avoidance for manned/unmanned aircraft cooperative system based on improved velociy obstacle method[J]. Journal of Northwestern Polytechnical University, 2020, 38(2): 309-318. (in Chinese) doi: 10.1051/jnwpu/20203820309 URL
[118]	李猛, 梁加红, 李石磊. 一种改进的多智能体碰撞避免行为[J]. 国防科技大学学报, 2013, 35(3): 92-98.
	LI M, LIANG J H, LI S L. An improved collision avoidance behavior of multiple agents[J]. Journal of National University of Defense Technology, 2013, 35(3): 92-98. (in Chinese)
[119]	贾高伟, 王建峰. 无人机集群任务规划方法研究综述[J]. 系统工程与电子技术, 2021, 43(1): 99-111.
	JIA G W, WANG J F. Research review of uav swarm mission planning method[J]. Systems Engineering and Electronics, 2021, 43(1): 99-111. (in Chinese)
[120]	FIORINI P, SHILLER Z. Motion planning in dynamic environments using velocity obstacles[J]. The international journal of robotics research, 1998, 17(7): 760-772. doi: 10.1177/027836499801700706 URL
[121]	VAN DEN BERG J, GUY S J, LIN M, et al. Reciprocal n-body collision avoidance[M]. PRADALIERC, SIEGWARTR, HIRZINGERG Robotics research. Berlin, Heidelberg, Germany: Springer, 2011: 3-19.
[122]	丁季时雨, 孙科武, 董博, 等. 基于元课程强化学习的多智能体协同博弈技术[J]. 现代防御技术, 2022, 50(5): 36-42.
	DING J S Y, SUN K W, DONG B, et al. Multi-agent autonomous cooperative confrontation based on meta curriculum reinforcement learning[J]. Modern Defence Technology, 2022, 50(5): 36-42. (in Chinese)
[123]	曹雷. 基于深度强化学习的智能博弈对抗关键技术[J]. 指挥信息系统与技术, 2019, 10(5): 1-7.
	CAO L. Key Technologies of intelligen game confrontation based on deep reinforcement learning[J]. Command Information System and Technology, 2019, 10(5): 1-7. (in Chinese)
[124]	郑健, 陈建, 朱琨. 基于多智能体强化学习的无人集群协同设计[J]. 指挥信息系统与技术, 2020, 11(6): 26-31.
	ZHENG J, CHEN J, ZHU K. Unmanned swarm cooperative design based on multi-agent reinforcement learning[J]. Command Information System and Technology, 2020, 11(6): 26-31. (in Chinese)
[125]	MYERSON R B. Game theory[M]. Cambridge, MA, US: Harvard university press, 2013.
[126]	OSBORNE M J. An introduction to game theory[M]. New York, NY, US: Oxford university press, 2004.
[127]	DING N, MENG X H, XIA W G, et al. Multivehicle coordinated lane change strategy in the roundabout under internet of vehicles based on game theory and cognitive computing[J]. IEEE Transactions on Industrial Informatics, 2019, 16(8): 5435-5443. doi: 10.1109/TII.2019.2959795 URL
[128]	RASMUSEN E. Games and information: an introduction to game theory[M]. 4th ed. Hoboken, NJ, S: John Wiley & Sons, Inc., 2006.
[129]	吴锋. 基于决策理论的多智能体系统规划问题研究[D]. 合肥: 中国科学技术大学, 2011.
	WU F. Decision-theoretic planning for multi-agent systems[D]. Hefei: University of Science and Technology of China, 2011. (in Chinese)
[130]	LIN D C, LI L, JABARI S E. Pay to change lanes: a cooperative lane-changing strategy for connected/automated driving[J]. Transportation Research Part C: Emerging Technologies, 2019, 105: 550-564. doi: 10.1016/j.trc.2019.06.006 URL
[131]	SUN X T, YIN Y F. Behaviorally stable vehicle platooning for energy savings[J]. Transportation Research Part C: Emerging Technologies, 2019, 99: 37-52. doi: 10.1016/j.trc.2018.12.017 URL
[132]	CALVO J A L, MATHAR R. Connected vehicles coordination: a coalitional game-theory approach[C]//Proceedings of 2018 European Conference on Networks and Communications. Ljubljana, Slovenia: IEEE, 2018: 1-6.
[133]	LIU Y, ZONG C F, HAN X J, et al. Spacing allocation method for vehicular platoon: a cooperative game theory approach[J]. Applied Sciences, 2020, 10(16): 5589. doi: 10.3390/app10165589 URL
[134]	GATTAMI A, AL ALAM A, JOHANSSON K H, et al. Establishing safety for heavy duty vehicle platooning: a game theoretical approach[J]. IFAC Proceedings Volumes, 2011, 44(1): 3818-3823. doi: 10.3182/20110828-6-IT-1002.02071 URL
[135]	AMAR H M, BASIR O A. A game theoretic solution for the territory sharing problem in social taxi networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(7): 2114-2124. doi: 10.1109/TITS.2018.2825654 URL
[136]	JI A, LEVINSON D. A review of game theory models of lane changing[J]. Transportmetrica A: Transport Science, 2020, 16(3): 1628-1647. doi: 10.1080/23249935.2020.1770368 URL
[137]	ALI Y, ZHENG Z D, HAQUE M M, et al. A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment[J]. Transportation research part C: emerging technologies, 2019, 106: 220-242. doi: 10.1016/j.trc.2019.07.011 URL
[138]	YOO J, LANGARI R. A predictive perception model and control strategy for collision-free autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(11): 4078-4091. doi: 10.1109/TITS.2018.2880409 URL
[139]	FABIANI F, GRAMMATICO S. Multi-vehicle automated driving as a generalized mixed-integer potential game[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(3): 1064-1073. doi: 10.1109/TITS.2019.2901505 URL
[140]	PHILIPPE C, ADOUANE L, TSOURDOS A, et al. Probability collectives algorithm applied to decentralized intersection coordination for connected autonomous vehicles[C]//Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France: IEEE, 2019: 1928-1934.
[141]	WANG Y W, REN Y, ELLIOTT S, et al. Enabling courteous vehicle interactions through game-based and dynamics-aware intent inference[J]. IEEE Transactions on Intelligent Vehicles, 2019, 5(2): 217-228.
[142]	DING N, MENG X H, XIA W G, et al. Multivehicle coordinated lane change strategy in the roundabout under internet of vehicles based on game theory and cognitive computing[J]. IEEE Transactions on Industrial Informatics, 2019, 16(8): 5435-5443. doi: 10.1109/TII.2019.2959795 URL
[143]	刘阳. 基于博弈论的车辆队列协同分层控制算法研究[D]. 长春: 吉林大学, 2020.
	LIU Y. Research on hierarchical control algorithm of motion cooperation for vehicle platoon based on game theory[D]. Changchun: Jilin University, 2020. (in Chinese)
[144]	REN W, BEARD R W. Distributed consensus in multi-vehicle cooperative control[M]. London, UK: Springer, 2008.
[145]	MIRHELI A, TAJALLI M, HAJIBABAI L, et al. A consensus-based distributed trajectory control in a signal-free intersection[J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 161-176. doi: 10.1016/j.trc.2019.01.004 URL
[146]	MOLINARI F, RAISCH J. Automation of road intersections using consensus-based auction algorithms[C]//Proceedings of 2018 Annual American Control Conference. Milwaukee, WI, US: IEEE, 2018: 5994-6001.
[147]	SANTINI S, SALVI A, VALENTE A S, et al. Platooning maneuvers in vehicular networks: a distributed and consensus-based approach[J]. IEEE Transactions on Intelligent Vehicles, 2018, 4(1): 59-72. doi: 10.1109/TIV.2018.2886677 URL
[148]	LI Y F, TANG C C, LI K Z, et al. Consensus-based cooperative control for multi-platoon under the connected vehicles environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(6): 2220-2229. doi: 10.1109/TITS.2018.2865575 URL
[149]	LI Y F, TANG C C, PEETA S, et al. Nonlinear consensus-based connected vehicle platoon control incorporating car-following interactions and heterogeneous time delays[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(6): 2209-2219. doi: 10.1109/TITS.2018.2865546 URL
[150]	YE F, GUO J L, KIM K J, et al. Bi-level optimal edge computing model for on-ramp merging in connected vehicle environment[C]//Proceedings of 2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France: IEEE, 2019: 2005-2011.
[151]	KULATUNGA A K, LIU D K, DISSANAYAKE G, et al. Ant colony optimization based simultaneous task allocation and path planning of autonomous vehicles[C]//Proceedings of 2006 IEEE Conference on Cybernetics and Intelligent Systems. Bangkok, Thailand: IEEE, 2006: 1-6.
[152]	PEI H X, FENG S, ZHANG Y, et al. A cooperative driving strategy for merging at on-ramps based on dynamic programming[J]. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11646-11656. doi: 10.1109/TVT.2019.2947192 URL