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  4. Vol 7, Issue 3, 2024

Research on path planning of patrol robot based on multi-algorithm fusion

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DOI: 10.23977/jaip.2024.070307 | Downloads: 16 | Views: 192

Author(s)

Xiancheng Fan 1, Yeqing Yu 1, Tao Li 1

Affiliation(s)

1 School of Electrical and Electronic Engineering, Anhui Institute of Information Technology, Wuhu, China

Corresponding Author

Xiancheng Fan

ABSTRACT

A multi-algorithm fusion path planning algorithm for patrol robots was proposed, In order to improve the robot's path planning ability, optimize the search efficiency, improve the robot's path smoothness and improve the control accuracy. The A* algorithm is optimized through the search field and heuristic function to optimize the node search, avoid the expansion of redundant nodes and improve the search efficiency of the algorithm while ensuring the optimal global path. The improved A* algorithm still has node redundancy, excessive path transition and other phenomena. Floyd algorithm is used to introduce improved A* key nodes to optimize the improved A* algorithm again, eliminate redundant nodes, smooth the global path, and dynamically increase the number of key nodes for long-distance key nodes to effectively prevent path deviation. In view of the shortcomings of the improved A* algorithm in dynamic obstacle planning, the improved DWA algorithm is integrated to achieve local path planning, and the integrated path planning algorithm has local dynamic and unknown environment obstacle avoidance ability. Experiments show that the proposed fusion algorithm has the ability of global path planning and local path planning, which verifies the feasibility and effectiveness of the fusion algorithm.

KEYWORDS

Patrol robots, Improved A*, Floyd algorithm, Improved DWA, Path planning

CITE THIS PAPER

Xiancheng Fan, Yeqing Yu, Tao Li, Research on path planning of patrol robot based on multi-algorithm fusion. Journal of Artificial Intelligence Practice (2024) Vol. 7: 48-61. DOI: http://dx.doi.org/10.23977/jaip.2024.070307.

REFERENCES

[1] Dudeja C, Kumar P. An improved weighted sum-fuzzy Dijkstra's algorithm for shortest path problem (iWSFDA) [J]. Soft Computing, 2022, 26(7): 647-676. 
[2] Tang G, Tang C, Claramunt C, et al. Geometric A-star algorithm: An improved A-star algorithm for AGV path planning in a port environment[J]. IEEEAccess, 2021, 9:59196-59210. 
[3] Zheng Chuanchuan, Ke Fuyang, Tang Qinqin. Research on Autonomous Navigation Simulation by Integrating Improved D * and Gmapping Algorithms [J]. Computer Simulation, 2023, 40(10): 452-457+518. 
[4] Lin Yifan, Chen Yanjie, He Bingwei, et al. Motion planning method for mobile robots without collision detection RRT *[J]. Journal of Instrumentation, 2020, 41(10): 257-267. 
[5] Lei Wu, Xiaodong Huang, Junguo Cui, et al. Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot[J]. Expert Systems with Applications, 2022, 215(1): 119410. 
[6] Huang Rongjie, Wang Yagang. Smooth Path Planning for Robots Based on Viewable and Improved Genetic Algorithm [J]. Control Engineering, 2024, 31(04): 678-686. 
[7] Chen Qiulian, Zheng Yijun, Jiang Huanyu, et al. Dynamic Path Planning Based on Neural Network Improved Particle Swarm Optimization [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49(02): 51-55. 
[8] Xu Wan, Cheng Zhao, Zhu Li, et al. A Local Path Planning Algorithm Based on Improved Artificial Potential Field Method [J]. Electronic Measurement Technology, 2022, 45(19): 83-88. 
[9] Haixu Yang; Xiaoming Xu; Jichao Hong. Automatic Parking Path Planning of Tracked Vehicle Based on Improved A* and DWA Algorithms [J]. IEEE, 2022, 9(1): 283-292. 
[10] Guo Zhijun, Yin Yakun, Li Yixuan, et al. Path planning for mobile robots by integrating improved A * and TEB algorithms [J]. Journal of Henan University of Science and Technology (Natural Science Edition), 2023, 44(04): 57-65+7. 
[11] TANG Zhuozhen, MA Hongzhong. An Overview of Path Planning Algorithms[J]. Iop Conference Series: Earth and Environmental Science, 2021, 804(2):022024. 
[12] Hu Zheng, Xu Bin. Dynamic Path Planning Integrating A * Algorithm and Artificial Potential Field Method [J]. Combination Machine Tool and Automation Processing Technology, 2023(07): 46-49+56. 
[13] Yin X, Cai P, Zhao K, Zhang Y, et al. Dynamic path planning of AGV based on kinematical constraint A* algorithm and following DWA fusion algorithms[J]. Sensors, 2023, 23(8):4102. 
[14] Li Sanping, Yuan Longqiang, Wu Liguo, et al. Path planning for mobile robots based on improved fusion ant colony algorithm [J]. Mechanical design, 2023, 40(10): 76-84. 
[15] Xiang D, Lin H, O Y J, et al. Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot[J]. Scientific Reports, 2022, 12(1):13273. 
[16] Chao Liu, Lei Wu, Guangxin Li, et al. Improved multi-search strategy A* algorithm to solve three-dimensional pipe routing design[J]. Expert Systems with Applications, 2024, 240: 122313. 
[17] Li C, Huang X, Ding J. Global path planning based on a bidirectional alternating search A* algorithm for mobile robots[J]. Computers & Industrial Engineering, 2022, 168:108123.
[18] Jiang H, Sun Y. Research on Global Path Planning of Electric Disinfection Vehicle Based on Improved A* Algorithm [J]. Energy Reports, 2021, 7:1270-1279. 
[19] Chao Liu, Lei Wu, Wensheng Xiao. An improved heuristic mechanism ant colony optimization algorithm for solving path planning [J]. Knowledge-based systems, 2023, 271:110540. 
[20] Yang Fangqing, Liu Jicheng. Path Planning for Mobile Robots by Integrating Improved A * Algorithm and Dynamic Window Method [J]. Industrial Control Computer, 2021, 34(5):106-108, 112. 
[21] Bian Yongming, Ji Pengcheng, Zhou Yihe, et a. Obstacle avoidance path planning for mobile robots based on improved DWA [J]. Chinese Journal of Engineering Machinery, 2021, 19(1):44-49. 
[22] Yang Guihua, Wei Jiale. Logistics robot path planning based on improved A * and DWA algorithms [J]. Science, Technology and Engineering, 2022, 22(34):15213-15220. 
[23] Wang Hongbin, Yin Pengheng, Zheng Wei, et al. Path planning for mobile robots based on improved A * algorithm and dynamic window method [J]. Robot, 2020, 42(3): 346-353.  
[24] Te Wang, Aijuan Li, Dongjin Guo, et al. Global Dynamic Path Planning of AGV Based on Fusion of Improved A* Algorithm and Dynamic Window Method[J]. Sensors, 2024, 24(6): 2011. 
[25] Liu Zhouhao, Wan Chaoyi, Yin Mingfeng, et al. Mobile robot path planning using improved A * algorithm and improved DWA algorithm [J]. Manufacturing automation, 2023, 45(12): 55-60.

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