The Research of Different Algorithms on Path Planning
DOI:
https://doi.org/10.54097/hset.v39i.6643Keywords:
Path Planning; Classical Algorithms; Graphics Algorithms; Bionics Algorithms.Abstract
The evolution of modern existence is inextricably linked to path planning. Road transportation, urban road planning, and travel plans for people's travel requirements must all be carried out and assured on the basis of effective route planning implementation. However, the path planning algorithms are not ideal. This work brings together three contemporary popular algorithms: classical algorithms, graphics algorithms, and bionics algorithms. The article presents and evaluates their functions, as well as summarizes the benefits and drawbacks of each method. Following that, the experimental data from each algorithm is gathered and compared. The performance difference between different types of methods is analyzed. The collected results are integrated with the prior analysis to form a summary. Finally, the paper summarizes and looks forward to the full text.
Downloads
References
Konakoglu, Berkant, Aydemir, Salih Berkan, Nature-Inspired Computation in Navigation and Routing Problems, Springer Science and Business Media LLC, 2020, 13(122): 532-538.
Peter J. M. Laarhoven, Emile H. L. Aarts, Simulated Annealing: Theory and Applications, 1988, 30( 3): 7–15.
Abdullah Alomari, William Phillips, Nauman Aslam and Frank Comeau, Dynamic Fuzzy-Logic Based Path Planning for Mobility-Assisted Localization in Wireless Sensor Networks, Sensors, 2017 17(1904).
Mohamed Tounsic, Elhadi Shakshukif and Habib Youssefg, On the Adequacy of Tabu Search for Global Robot Path Planning Problem in Grid Environments, 5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014), Procedia Computer Science 2014, 32: 604 – 613.
Weiwei Li, Huarong Xu, Guanhua Chen, Jianhong Qian, Xinping Wen. Research on Optimization of Intelligent Public Transportation Scheduling Based on Big Data, Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition, 2020: 235-248.
Farid Bounini, Denis Gingras, Hervé Pollart and Dominique Gruyer, Modified Artificial Potential Field Method for Online Path Planning Applications. 2017,438 (2):483-491.
Hui Liu. Rail transit inspection unmanned aerial vehicle (UAV) systems, Elsevier BV, 2020: 1-11.
Alex Nash, Kenny Daniel and Sven Koenig Any-Angle Path Planning on Grids, AAAI, 2007,438 (71):121-129.
Lehua Yang, Dongmei Li, Ruipu Tan. Research on the Shortest Path Solution Method of Interval Valued Neutrosophic Graphs Based on the Ant Colony Algorithm, IEEE Access, 2020 2337-2349.
Marco Dorigo, Ant colony optimization Scholarpedia, 2007, 2(3):1461.
W. F. Van Gunsteren and H. J. C. Berendsen, A Leap-frog Algorithm for Stochastic Dynamics, Molecular Simulation, 2021, 1(3) 173 -185.
Haibin Duan, Linzhi Huang, Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning, Neurocomputing, 2014:1-11.
Hui Miao and Yu-Chu Tian, Robot path planning in dynamic environments using a simulated annealing based approach. Soh, Y C & Nguyen, K S (Eds.) Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008. IEEE Computer Society, United States, 2008. 1253-1258.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
