Hostname: page-component-5b777bbd6c-w9n4q Total loading time: 0 Render date: 2025-06-20T05:32:41.935Z Has data issue: false hasContentIssue false
  • English
  • Français

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

Published online by Cambridge University Press:  29 November 2023

Xianxing Tang Open the ORCID record for Xianxing Tang [Opens in a new window] ,
Haibo Zhou  and
Tianying Xu
Xianxing Tang
Affiliation:
School of Mechanical and Electrical Engineering, Central South University, Changsha, Hunan, China State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, Hunan, China
Haibo Zhou*
Affiliation:
School of Mechanical and Electrical Engineering, Central South University, Changsha, Hunan, China State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, Hunan, China
Tianying Xu
Affiliation:
School of Mechanical and Electrical Engineering, Central South University, Changsha, Hunan, China State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, Hunan, China
*
Corresponding author: Haibo Zhou; Email: zhouhaibo@csu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

Keywords

6-DOF robotic armobstacle avoidancepath optimizationartificial potential field method
Type
Research Article
Information
Robotica , Volume 42 , Issue 2 , February 2024 , pp. 457 - 481
Copyright
© Central South University, 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Xu, T., Zhou, H., Tan, S., Li, Z., Ju, X. and Peng, Y., Mechanical arm obstacle avoidance path planning based on improved artificial potential field method,” Ind. Robot. 2, 49 (2022).Google Scholar
kheireddine, C., Yassine, A., Fawzi, S. and Khalil, M., “A robust synergetic controller for Quadrotor obstacle avoidance using Bézier curve versus B-spline trajectory generation,” Intel. Serv. Robot. 15(1), 143–15227 (2022).CrossRefGoogle Scholar
Trinh, L. A., Ekström, M. and Cürüklü, B., “Dependable navigation for multiple autonomous robots with petri nets based congestion control and dynamic obstacle avoidance,” J. Intell. Robot. Syst. 104(4), 69 (2022).CrossRefGoogle Scholar
He, Z., Liu, C., Chu, X., Negenborn, R. R. and Wu, Q., “Dynamic anti-collision A-star algorithm for multi-ship encounter situations,” Appl. Ocean. Res. 118, 102995 (2022).CrossRefGoogle Scholar
Ou, J., Hong, S. H., Ziehl, P. and Wang, Y., “GPU-based global path planning using genetic algorithm with near corner initialization,” J. Intell. Robot. Syst. 104(2), 34 (2022).CrossRefGoogle Scholar
Fang, Z. and Liang, X., “Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method,” Ind. Robot. 49(5), 835–850 (2022).CrossRefGoogle Scholar
Lei, W., Ming, L., Dunbing, T. and Jingcao, C., “Dynamic path planning for mobile robot based on improved genetic algorithm,” J. Nanjing Univ. Aeronaut. Astronaut. 48(06), 841–846 (2016).Google Scholar
Elhoseny, M., Tharwat, A. and Hassanien, A. E., “Bezier curve based path planning in a dynamic field using modified genetic algorithm,” J. Comput. Sci. 25, 339–350 (2018).CrossRefGoogle Scholar
Rs, A., Db, B. and Nc, A., “Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets,” J. King Saud Univ. Comput. Inf. Sci. 34(7), 4269–4283 (2022).Google Scholar
Suresh, K. S., Venkatesan, R. and Venugopal, S., “Mobile robot path planning using multi-objective genetic algorithm in industrial automation,” Soft Comput. 26(15), 7387–7400 (2022).CrossRefGoogle Scholar
Kennedy, J. and Eberhart, R.. Particle swarm optimization. In: Proceedings of ICNN International Conference on Neural Networks, 4, (1995) pp. 1942–1948.Google Scholar
Tang, B., Zhu, Z. and Luo, J., “A convergence-guaranteed particle swarm optimization method for mobile robot global path planning,” Assembly Autom. 37(1), 114–129 (2017).CrossRefGoogle Scholar
Jia, H. Q., Wei, Z., He, X. and Zhang, L., “Path planning based on improved particle swarm optimization algorithm,” Trans. Chin. Soc. Agric. Machin. 49(12), 371–377 (2018).Google Scholar
Song, B., Wang, Z. and Zou, L., “An improved pso algorithm for smooth path planning of mobile robots using continuous high-degree bezier curve,” Appl. Soft Comput. 100(1), 106960 (2021).CrossRefGoogle Scholar
Hansen, L. K. and Salamon, P., “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (2002).CrossRefGoogle Scholar
Miao, C., Chen, G., Yan, C. and Wu, Y., “Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm,” Comput. Ind. Eng. 156, 107230 (2021).CrossRefGoogle Scholar
Pérez-Cutiño, M. A., Rodríguez, F., Pascual, L. D. and Díaz-Báñez, J. M., “Ornithopter trajectory optimization with neural networks and random forest,” J. Intell. Robot. Syst. 105(1), 17 (2022).CrossRefGoogle Scholar
Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” IEEE Int. Conf. Robot. Autom. 2, 500–505 (1985).Google Scholar
Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton. Robot. 13(3), 207–222 (2002).CrossRefGoogle Scholar
Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cyb. 4(2), 100–107 (1968).CrossRefGoogle Scholar
Pal, A., Tiwari, R. and Shukla, A.. Multi Robot Exploration Using a Modified A* Algorithm. In: International Conference on Intelligent Information & Database Systems, Springer-Verlag (2011).CrossRefGoogle Scholar
Ren, Y. Y., Song, X. R. and Song, G.. Research on Path Planning of Mobile Robot Based on Improved A* in Special Environment. In: IEEE International Symposium on Autonomous Systems, Shanghai, China (2019) pp. 12–16.Google Scholar
Guruji, A. K., Agarwal, H. and Parsediya, D. K., “Time-efficient A* algorithm for robot path planning,” Proced. Technol. 23, 144–149 (2016).CrossRefGoogle Scholar
Li, C., Huang, X., Ding, J., Song, K. and Lu, S., “Global path planning based on a bidirectional alternating search A* algorithm for mobile robots,” Comput. Ind. Eng. (168-), 168 (2022).Google Scholar
Zuo, L., Guo, Q., Xu, X. and Fu, H., “A hierarchical path planning approach based on A* and least-squares policy iteration for mobile robots,” Neurocomputing 170(dec.25), 257–266 (2015).CrossRefGoogle Scholar
Wang, S. K. and Zhu, L., “Motion planning method for obstacle avoidance of 6-DOF manipulator based on improved A* algorithm,” J. Donghua Univ. (Eng. Ed.) 32(1), 7 (2015).Google Scholar
Bing, F., Lin, C., Zhou, Y., D. Zheng, Z. Wei, J. Dai and H. Pan, “An improved A* algorithm for the industrial robot path planning with high success rate and short length,” Robot. Auton. Syst. 106, 26–37 (2018).Google Scholar
Newman, W. S. and Branicky, M. S., “Real-time configurations for space transforms for obstacle avoidance,” Int. J. Robot. Res. 10(6), 650–667 (1991).CrossRefGoogle Scholar
Jia, Q., “Path planning for space manipulator to avoid obstacle based on A* algorithm,” J. Mech. Eng. 46(13), 109 (2010).CrossRefGoogle Scholar
Zhang, N., Zhang, Y., Ma, C. and Wang, B.. Path planning of six-DOF serial robots based on improved artificial potential field method. In: 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2017).CrossRefGoogle Scholar
Zhang, L., Zhang, Y., Zeng, M. and Li, Y., “Robot navigation based on improved A* algorithm in dynamic environment,” Assembly Autom. 41(4), 419–430 (2021).CrossRefGoogle Scholar
Wang, X., Tang, M., Dinesh, M. and Tong, R., “Efficient BVH-based collision detection scheme with ordering and restructuring,” Comput. Graph. Forum 37(2), 227–237 (2018).CrossRefGoogle Scholar
Liu, H., Qu, D., Xu, F., Du, Z., Jia, K., Song, J. and Liu, M., “Real-time and efficient collision avoidance planning approach for safe human-robot interaction,” J. Intell. Robot. Syst. 105(4), 93 (2022).CrossRefGoogle Scholar