A Survey on Collision Avoidance for Multi-robot Systems

被引：0
作者：
Park J. [1 ]
Oh D. [2 ]
Kim H.J. [2 ]
机构：
[1] Department of Mechanical System Design Engineering, Seoul National University of Science and Technology
[2] Department of Aerospace Engineering and Automation and Systems Research Institute, Seoul National University
来源：
Journal of Institute of Control, Robotics and Systems | 2024年 / 30卷 / 04期
基金：
新加坡国家研究基金会;
关键词：
collision avoidance; multi-robot systems; path planning; reinforcement learning; survey;
D O I：
10.5302/J.ICROS.2024.24.0033
中图分类号：
学科分类号：
摘要：
Multi-robot systems (MRS) enable cooperation between multiple robots to achieve common goals or tasks. These systems can enhance efficiency and productivity in various applications, such as transportation, manufacturing, and exploration. However, a critical issue in MRS operation is the possibility of collisions between robots or with static/dynamic obstacles. This survey presents the latest trends and advancements in collision avoidance approaches for multi-robot systems. We analyze centralized and distributed collision avoidance methods, examining the overall performance, applicable vehicle platforms, and the necessity for inter-robot communication. This survey also explores the applicability of reinforcement learning-based methods for collision avoidance in multi-agent systems. © ICROS 2024.
引用
收藏
页码：402 / 411
页数：9
相关论文
共 90 条
[1]  
Mellinger D., Kushleyev A., Kumar V., Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams, Proc. of 2012 IEEE International Conference on Robotics and Automation, pp. 477-483, (2012)
[2]  
Augugliaro F., Schoellig A.P., D'Andrea R., Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach, Proc. of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1917-1922, (2012)
[3]  
Chen Y., Cutler M., How J.P., Decoupled multiagent path planning via incremental sequential convex programming, Proc. of 2015 IEEE International Conference on Robotics and Automation, pp. 5954-5961, (2015)
[4]  
Richards A., How J., Decentralized model predictive control of cooperating UAVs, 2004 43Rd IEEE Conference on Decision and Control, 4, (2004)
[5]  
Robinson D.R., Mar R.T., Estabridis K., Hewer G., An efficient algorithm for optimal trajectory generation for heterogeneous multi-agent systems in non-convex environments, IEEE Robotics and Automation Letters, 3, 2, pp. 1215-1222, (2018)
[6]  
Honig W., Preiss J.A., Kumar T.K.S., Sukhatme G.S., Ayanian N., Trajectory planning for quadrotor swarms, IEEE Transactions on Robotics, 34, 4, pp. 856-869, (2018)
[7]  
Debord M., Honig W., Ayanian N., Trajectory planning for heterogeneous robot teams, Proc. of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 7924-7931, (2018)
[8]  
Park J., Kim H.J., Fast trajectory planning for multiple quadrotors using relative safe flight corridor, Proc. of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 596-603, (2019)
[9]  
Park J., Kim J., Jang I., Kim H.J., Efficient multi-agent trajectory planning with feasibility guarantee using relative Bernstein polynomial, Proc. of 2020 IEEE International Conference on Robotics and Automation, pp. 434-440, (2020)
[10]  
Sharon G., Stern R., Felner A., Sturtevant N.R., Conflict-based search for optimal multi-agent pathfinding, Artificial Intelligence, 219, pp. 40-66, (2015)
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