Path Planning Algorithm for Mobile Robots
DOI:
https://doi.org/10.62051/ajnbr762Keywords:
Mobile robot; Path planning; Algorithm optimization; Algorithm classification and summary.Abstract
Path planning algorithm is a critical technology enabling mobile robots to realize autonomous navigation. In this paper, the path-planning technology of mobile robots is deeply discussed, and the operation mechanism and principle of different algorithms are analyzed in detail. Based on the understanding of the characteristics of mobile robot path planning algorithms, these algorithms are divided into three categories: traditional planning algorithm, intelligent planning algorithm, and sampling-based planning algorithm. This paper reviews and discusses the key research results in recent years, especially the advantages and limitations of various algorithms, which are analyzed in depth. Considering the current research status of mobile robot path planning technology, this paper also forecasts future research trends to provide new thinking direction for further development, such as the mixed-use of different types of path planning algorithms, the adoption of new technologies such as deep learning for path planning, and the application of multi-robot collaborative path planning.
Downloads
References
O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research 2 (1985) 500 - 505.
G. Li, A. Yamashita, H. Asama, et al. An efficient improved artificial potential field-based regression search method for robot path planning. International Conference on Mechatronics &. Automation. IEEE, 9 (8) (2012) 9 - 13.
Y. Cong, Z. Zhao, C. Na, et al. Dynamic obstacle avoidance path planning for unmanned aerial vehicles based on improved artificial potential field. Journal of Weapon Equipment Engineering, 42 (9) (2021) 7.
Q. Bo, S. Liang, C. Lu, et al., Dual robotic arm collision avoidance path planning based on improved artificial potential field method for bidirectional planning. Journal of Jiangsu University of Science and Technology: Natural Science Edition, 35 (5) (2021) 8.
B. Wang, H. Wu, X. Niu, Robot path planning based on improved potential field method. Computer Science. Computer Measurement and Control, 27 (1) (2019) 1 - 12.
W. Shi, X. Huang, W. Zhou, Mobile robot path planning based on improved artificial potential field method. Computer Application, (8) (2010) 3.
D. B. Johnson, A note on Dijkstra's shortest path algorithm. Journal of the ACM, 20 (3) (1973) 385 - 388.
Q. Li, B. Li, R. Zhang, et al. Research on AGV path planning based on improved Dijkstra algorithm CJ Mechanical Engineering and Automation, Computer Simulation. 2021 (1) (2021) 23 - 25.
C. Zhang, X. Li, X. Zhao, Transmission line path planning based on improved Dijkstra algorithm. Electric Power Survey and Design, 2022 (2) (2022) 1 - 5.
J. Gong, Z. Niu, Y. Zhang, Multi objective path planning for campus food delivery robots based on local dimensionality reduction Dijkstra algorithm. Journal of Shandong University of Technology (Natural Science Edition), 35 (4) (2021) 75 - 80.
M. Guo, T. Shi, Research on robot path planning based on improved Dijkstra algorithm lj - electrical technology, Computer measurement and control 20 (20) (2020) 22 - 23.
W. Jia, W. Wei, L. Zhu, et al. Research on path planning and visualization of mobile robots under improved Dijkstra algorithm. Journal of Xuzhou University of Engineering (Natural Science Edition), 36 (2) (2021) 34 - 38.
P. E. Hart, N. J. Nilsson, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science & Cybernetics, 4 (2) (1972) 28 - 29.
A. Stentz, Optimal and efficient path planning for partially known environments. Robotics and Automation, 1994.Proceedings. IEEE International Conference on IEEE, 14 (6). (1994) 14 - 16.
H. J. Bremermann, The evolution of intelligence: The nervous system as a model of its environment. University of Washington, Department of Mathematics, 1958.
H. Wang, X. Zhao, X. Yuan, Robot path planning based on improved adaptive genetic algorithm. Electro Optics and Control (2022) 1 - 7.
J.Wang, X. Wang, Q. Tian, et al. Path planning for mobile robots based on improved fuzzy adaptive genetic algorithm. Machine Tool and Hydraulic, 49 (23) (2021) 18 - 23.
M. Nazarahari, E. Khanmirza, S. Doostie, Multi-objective multi-robot path planning in continuous environment using an enhanced Genetic Algorithm. Expert Systems with Applications, 115 (2018) 106 - 120.
J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948, IEEE, Perth, Australia, 1995.
E. Kavraki, P. Svestka, J. C. Latombe, et al., Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12 (4) (1996) 566 - 580.
A. A. Ravankar, A. Ravankar, T. Emaru, et al. HP-PRM: Hybrid potential-based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots. IEEE Access, 8 (2020) 221743 - 221766.
J. Zhong, J. Su, Robot path planning in narrow passages based on probabilistic roadmaps. International Journal of Robotics and Automation, 28 (3) (2013) 29 - 32.
Q. Cheng, S. Gao, K. Cao, et al. Path planning for mobile robots based on PRM optimization algorithm. Computer Applications and Software, 37 (12) (2020) 254 - 259.
S. M. LaValle, Rapidly-exploring random trees: A new tool for path planning. IEEE International Conference on Robotics and Automation. IEEE. 1998.
Downloads
Published
Conference Proceedings Volume
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.