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  4. Vol 7, Issue 4, 2024

Literature Review of Path Planning Algorithms for Mobile Robots

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DOI: 10.23977/jaip.2024.070417 | Downloads: 42 | Views: 389

Author(s)

Leiwen Yuan 1

Affiliation(s)

1 Yunnan Normal University, Kunming, China

Corresponding Author

Leiwen Yuan

ABSTRACT

Path planning is a very important part of the working process of mobile robots, and quickly and efficiently planning a feasible path is currently a research focus. Excellent path planning algorithms can save a lot of time and economic costs. To comprehensively understand the development of mobile robot path planning technology, this article elaborates on the classic global path planning algorithms and local path planning algorithms both domestically and internationally. According to the properties of mobile robot path planning algorithms, they are divided into global path planning algorithms and local path planning algorithms. The global path planning is further divided into sampling based, search based, and biomimetic based planning algorithms, and the development of various algorithms is introduced. The current status of mobile robot path planning algorithms is summarized, and the future prospects are also discussed.

KEYWORDS

Path Planning, Mobile Robots, RRT, Sampling Planning, DWA

CITE THIS PAPER

Leiwen Yuan, Literature Review of Path Planning Algorithms for Mobile Robots. Journal of Artificial Intelligence Practice (2024) Vol. 7: 136-141. DOI: http://dx.doi.org/10.23977/jaip.2024.070417.

REFERENCES

[1] Touzani H, Hadj-Abdelkader H, Séguy N, et al. Multi-robot task sequencing & automatic path planning for cycle time optimization: Application for car production line[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1335-1342. 
[2] Vougioukas S. Annual review of control, robotics, and autonomous systems[J]. Agricultural robotics, 2019, 2(1): 365-392.
[3] Wang M, Zhang L, Wang Z, et al. A real-time dynamic trajectory planning for autonomous driving vehicles[C]// 2019 3rd Conference on Vehicle Control and Intelligence (CVCI). IEEE, 2019: 1-6.
[4] Quan L, Han L, Zhou B, et al. Survey of UAV motion planning[J]. IET Cyber‐systems and Robotics, 2020, 2(1): 14-21.
[5] Geraerts R, Overmars M H. A comparative study of probabilistic roadmap planners[M]//Algorithmic foundations of robotics V. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004: 43-57.
[6] LaValle S M, Kuffner Jr J J. Randomized kinodynamic planning[J]. The international journal of robotics research, 2001, 20(5): 378-400.
[7] Karaman S, Frazzoli E. Sampling-based algorithms for optimal motion planning[J]. The international journal of robotics research, 2011, 30(7): 846-894.
[8] Kuffner J J, LaValle S M. RRT-connect: An efficient approach to single-query path planning[C]//Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065). IEEE, 2000, 2: 995-1001.
[9] Gammell J D, Srinivasa S S, Barfoot T D. Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic[C]//2014 IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2014: 2997-3004.
[10] Dijkstra E W. A note on two problems in connexion with graphs[M]//Edsger Wybe Dijkstra: his life, work, and legacy. 2022: 287-290.
[11] Hart P E, Nilsson N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE transactions on Systems Science and Cybernetics, 1968, 4(2): 100-107.
[12] Kennedy J, Eberhart R. Particle swarm optimization[C]//Proceedings of ICNN'95-international conference on neural networks. ieee, 1995, 4: 1942-1948.
[13] Holland J H. Outline for a logical theory of adaptive systems[J]. Journal of the ACM (JACM), 1962, 9(3): 297-314.
[14] Fox D, Burgard W, Thrun S. The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23-33.
[15] Quinlan S, Khatib O. Elastic bands: Connecting path planning and control[C]//[1993] Proceedings IEEE International Conference on Robotics and Automation. IEEE, 1993: 802-807.
[16] Rösmann C, Feiten W, Wösch T, et al. Trajectory modification considering dynamic constraints of autonomous robots[C]//ROBOTIK 2012; 7th German Conference on Robotics. VDE, 2012: 1-6.
[17] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[J]. The international journal of robotics research, 1986, 5(1): 90-98. 
[18] Miao C, Chen G, Yan C, et al. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm[J]. Computers & Industrial Engineering, 2021, 156: 107230.
[19] Liu J, Anavatti S, Garratt M, et al. Modified continuous ant colony optimisation for multiple unmanned ground vehicle path planning[J]. Expert Systems with Applications, 2022, 196: 116605.

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