Analysis on Current Development Situation of Unmanned Ground Vehicle Clusters Collaborative Pursuit

被引：0
|
作者：
Xu Y. [1 ]
Guo H. [1 ]
Lou J. [1 ]
Ye P. [1 ]
Su Z. [1 ]
机构：
[1] Army Military Transportation University, Tianjin
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 02期
关键词：
Collaborative pursuit; Roundup; Search; Strategy mechanism; Tracking; Unmanned ground vehicle clusters;
D O I：
10.11999/JEIT230122
中图分类号：
学科分类号：
摘要：
In recent years, there has been a growing interest in unmanned ground vehicle clustering as a research topic in the unmanned driving field for its low cost, good secuity, and high autonomy. Various collaborative strategies have been proposed for unmanned vehicle clusters, with collaborative pursuit being a particularly important application direction that has garnered significant attention in various fields. A systematic analysis of the strategy mechanism for collaborative pursuit in unmanned vehicle clusters is provided, considering relevant applications and architectures. The collaborative pursuit strategy is divided into three sub-modes: search, tracking, and roundup. The key methods for unmanned vehicle cluster collaborative pursuit are compared from the perspectives of game theory, probabilistic analysis, and machine learning, the advantages and disadvantages of these algorithms are highlighted. Finally, comments and suggestions are provided for future research, considering offer references and ideas for further improving the efficiency and performance of collaborative pursuit in unmanned vehicle clusters. © 2024 Science Press. All rights reserved.
引用
收藏
页码：456 / 471
页数：15
相关论文
共 97 条
  • [71] ZHONG Yun, YAO Peiyang, SUN Yu, Et al., Method of multi-UAVs cooperative search for markov moving targets[C], 2017 29th Chinese Control And Decision Conference (CCDC), pp. 6783-6789, (2017)
  • [72] OLOFSSON J, HENDEBY G, LAUKNES T R, Et al., Multi-agent informed path planning using the probability hypothesis density[J], Autonomous Robots, 44, 6, pp. 913-925, (2020)
  • [73] LEONARDOS S, OVERMAN W, PANAGEAS I, Et al., Global convergence of multi-agent policy gradient in markov potential games, The Tenth International Conference on Learning Representations, (2021)
  • [74] ZHU Xudong, ZHANG Fan, Hui LI, Swarm deep reinforcement learning for robotic manipulation[J], Procedia Computer Science, 198, pp. 472-479, (2022)
  • [75] ZHANG Zheng, WANG Xiaohan, ZHANG Qingrui, Et al., Multi-robot cooperative pursuit via potential field-enhanced reinforcement learning[C], 2022 International Conference on Robotics and Automation (ICRA), pp. 8808-8814, (2022)
  • [76] HU Jian, HU Siyue, LIAO S W., Policy regularization via noisy advantage values for cooperative multi-agent actor-critic methods, (2021)
  • [77] LI Ruizhen, YANG Huizhen, XIAO Congshan, Cooperative hunting strategy for multi-mobile robot systems based on dynamic hunting points[J], Control Engineering of China, 26, 3, pp. 510-514, (2019)
  • [78] OLSEN T, STIFFLER N M, O'KANE J M., Rapid recovery from robot failures in multi-robot visibility-based pursuit-evasion[C], 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9734-9741, (2021)
  • [79] ZHANG Wenyu, GAI Jingyao, ZHANG Zhigang, Et al., Double-DQN based path smoothing and tracking control method for robotic vehicle navigation, Computers and Electronics in Agriculture, 166, (2019)
  • [80] QADIR M Z, PIAO Songhao, JIANG Haiyang, Et al., A novel approach for multi-agent cooperative pursuit to capture grouped evaders[J], The Journal of Supercomputing, 76, 5, pp. 3416-3426, (2020)
12345678910