Jiading Yang
ABSTRACT
In order to improve the efficiency of the mobile robot and select a better path planning algorithm suitable for obstacle scenes, the artificial potential field method ( APF ) based on the annealing algorithm and A* algorithm are compared under different obstacles. The two algorithms are simulated in three different complexity scenarios. The results show that the two algorithms perform well in the narrow channel at the target point, in the single model with fewer obstacles, the artificial potential field method has fewer corners and shorter paths. For L-shaped and hill-shaped complex scenes, A* can accurately find shorter paths, and the artificial potential field method is prone to fall into local traps, however, the relatively simple obstacles can be handled by the annealing algorithm.
- Khatib. 1986. Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 1986,1(5): 90-98. https://doi.org/10.1109/ROBOT.1985.1087247.Google Scholar
- Chen P, Li M, Mou J . 2021. A Velocity Obstacle-Based Real-Time Regional Ship Collision Risk Analysis Method. Journal of Marine Science and Engineering, 2021,9(4). https://doi.org/10.3390/jmse9040428.Google Scholar
- Xiaomin Z, Jianjun Y, Hongkai D, Velocity Obstacle Based on Vertical Ellipse for Multi-Robot Collision Avoidance. Journal of Intelligent & Robotic Systems, 2020,99:183-208. https://doi.org/10.1007/s10846-019-01127-6.Google Scholar
- WANG Hao, ZHAO Xuejun, YUAN Xiujiu. 2022. Robot Path Planning Based on lmproved Adaptive Genetic Algorithm. Electronics Optics & Control, 2022: 1-7.Google Scholar
- Gong Yuehong, Zhang Shaojun, Wang Mingyu, 2022. USV path planning method based on GA-PSO. Journal of Shandong Jiaotong University, 2022,30(01): 29-34. https://doi.org/10.3969/j.issn.1672-0032.2022.01.005.Google Scholar
- Yang Zhou, Liu Haibin. 2021. AGV Dynamic Path Planning Based on lmproved Ant Colony Algorithm and Dynamic Window Approach. Computer Engineering and Applications, 2021:1-11.Google Scholar
- Liu Shun, Yan Ronglan, Tai Haojiao, 2022. Path Planning of Agricultural Wheeled Robot Based on Local Potential Field A∼* Algorithm and Dynamic-Window Method. Jiangsu Agricultural Sciences, 2022,50(02): 192-198.Google Scholar
- Li Xiang, Min Dequan, Zhang Qi. 2020. Vehicle routing optimization of semi-open cold chain logistics under random Demand. Packaging Engineering, 2020:1-14.Google Scholar
- Paola B, Matthieu J, Federico L, 2019. Degree-Greedy Algorithms on Large Random Graphs. ACM SIGMETRICS Performance Evaluation Review, 2019,46(3). https://doi.org/10.1145/3308897.3308910.Google ScholarDigital Library
- Wang Qiang, Zhang An, Wu Zhongjie. 2014. Improved artificial potential field method and simulated annealing algorithm for UAV route planning. Fire control & command control, 2014,39(08):70-73. https://doi.org/ 10.3969/j.issn.1002-0640.2014.08.017.Google Scholar
- Wan Ping. 2018. Route Planning Algorithm Design and Simulation Research Based on A-Star Algorithm. China Water Transport. Waterway Science and Technology, 2018(04):58-65. https://doi.org/10.19412/j.cnki.42-1395/u.2018.04.013.Google Scholar
- Han Yao, Li Shaohua. 2021. Trajectory planning of UAV based on improved artificial potential field method. System Engineering and Electronics, 2021,43(11): 3305-3311. https://doi.org/10.12305/j.issn.1001-506X.2021.11.31.Google Scholar
- Xu Xiaoqiang, WANG Mingyong, MAO Yan. 2020. Path planning for mobile robot based on improved artificial potential field method. Journal of Computer Applications, 2020,40(12): 3508-3512. https://doi.org/10.11772/j.issn.1001-9081.2020050640.Google Scholar
- Lu Yuting, Lin Yuyou, Peng Qiaozi, 2015. Review of simulated annealing algorithm improvement and parameter exploration. College Mathematics, 2015,31(06): 96-103. https://doi.org/10.3969/j.issn.1672-1454.2015.06.020.Google Scholar
- Lin Hanxi, Xiang Dan, Ouyang Jian, 2021. Review of Path Planning Algorithms for Mobile Robots. Computer Engineering and Applications, 2021,57(18): 38-48. https://doi.org/10.3778/j.issn.1002-8331.2103-0519.Google Scholar
- Song Yu, Gu Haijiao. 2020. UAV route planning based on improved A∼* algorithm. Journal of changchun university of technology, 2020,41(06): 597-601. https://doi.org/10.15923/j.cnki.cn22-1382/t.2020.6.14.Google Scholar
Recommendations
Research on Path Planning Based on Artificial Potential Field Algorithm
AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced ManufactureArtificial potential field (APF) algorithm is widely used in path planning research because of its simple structure, good real-time performance and smooth path generated to solve the problem of obstacle avoidance in task space environment. Aiming at the ...
Obstacle Avoidance Path Planning for Double Manipulators Based on Improved Artificial Potential Field Method
ICITEE '19: Proceedings of the 2nd International Conference on Information Technologies and Electrical EngineeringIn this paper, an obstacle avoidance path planning algorithm based on improved artificial potential field method is proposed for a double six-degree-of-freedom manipulator. In the first, obstacle avoidance motion programming is carried out for the main ...
Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning
ISCID '14: Proceedings of the 2014 Seventh International Symposium on Computational Intelligence and Design - Volume 02This paper presents an adaptive artificial potential field method for robot's obstacle avoidance path planning. Despite the obstacle avoidance path planning based on the artificial potential field method is very popular, but there is local minima ...
Comments