A Review of Path-Planning Approaches for Multiple Mobile Robots

被引:77
作者
Lin, Shiwei [1 ]
Liu, Ang [1 ]
Wang, Jianguo [1 ]
Kong, Xiaoying [2 ]
机构
[1] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
[2] Melbourne Inst Technol, Sch IT & Engn, Sydney Campus, Sydney, NSW 2000, Australia
关键词
multi-robot path planning; bio-inspired algorithms; robots; PIGEON-INSPIRED OPTIMIZATION; SELF-ORGANIZING MAP; TASK ASSIGNMENT; MULTIAGENT PICKUP; AGV SYSTEMS; MULTIROBOT; ALGORITHM; COORDINATION; ENVIRONMENTS; EXPLORATION;
D O I
10.3390/machines10090773
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Numerous path-planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning approaches and decision-making strategies and presents the path-planning algorithms for various types of robots, including aerial, ground, and underwater robots. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. The decision-making strategies mainly consist of centralized and decentralized approaches. The trend of the decision-making system is to move towards a decentralized planner. Finally, the new challenge in multi-robot path planning is proposed as fault tolerance, which is important for real-time operations.
引用
收藏
页数:27
相关论文
共 168 条
[91]   Complete Coverage Path Planning for a Multi-UAV Response System in Post-Earthquake Assessment [J].
Nedjati, Arman ;
Izbirak, Gokhan ;
Vizvari, Bela ;
Arkat, Jamal .
ROBOTICS, 2016, 5 (04)
[92]  
Nielsen I., 2020, P 17 INT C DISTR COM, P5, DOI [10.1007/978-3-030-53829-3_1, DOI 10.1007/978-3-030-53829-3_1]
[93]  
Okumura K, 2022, Arxiv, DOI [arXiv:2105.07132, 10.48550/arXiv.2105.07132, DOI 10.48550/ARXIV.2105.07132]
[94]   Multi-agent informed path planning using the probability hypothesis density [J].
Olofsson, Jonatan ;
Hendeby, Gustaf ;
Lauknes, Tom Rune ;
Johansen, Tor Arne .
AUTONOMOUS ROBOTS, 2020, 44 (06) :913-925
[95]  
Olofsson J, 2017, 2017 WORKSHOP ON RESEARCH, EDUCATION AND DEVELOPMENT OF UNMANNED AERIAL SYSTEMS (RED-UAS), P13, DOI 10.1109/RED-UAS.2017.8101636
[96]   Optimal bid valuation using path finding for multi-robot task allocation [J].
Ozturk, Savas ;
Kuzucuoglu, Ahmet Emin .
JOURNAL OF INTELLIGENT MANUFACTURING, 2015, 26 (05) :1049-1062
[97]   Optimal Multi-robot Path Planning Using Particle Swarm Optimization Algorithm Improved by Sine and Cosine Algorithms [J].
Paikray, H. K. ;
Das, P. K. ;
Panda, S. .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2021, 46 (04) :3357-3381
[98]   Hybridization of IWO and IPSO for mobile robots navigation in a dynamic environment [J].
Panda, Mohit Ranjan ;
Das, Pradipta ;
Pradhan, Saroj .
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2020, 32 (09) :1020-1033
[99]   Hybridizing Invasive Weed Optimization with Firefly Algorithm for Multi-Robot Motion Planning [J].
Panda, Mohit Ranjan ;
Dutta, Shubham ;
Pradhan, Saroj .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (08) :4029-4039
[100]   Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm [J].
Pandey, Anish ;
Parhi, Dayal R. .
DEFENCE TECHNOLOGY, 2017, 13 (01) :47-58