Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-29T15:33:04.010Z Has data issue: false hasContentIssue false
  • English
  • Français

Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

Published online by Cambridge University Press:  04 January 2024

Dong Yang Open the ORCID record for Dong Yang [Opens in a new window] ,
Li Dong  and
Jun Kang Dai
Dong Yang*
Affiliation:
School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China
Li Dong
Affiliation:
School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China Hefei Xinsheng Optoelectronics Technology Corporation Ltd, Hefei, 230012, China
Jun Kang Dai
Affiliation:
Hefei cement Research & Design Institute Corporation Ltd, Hefei, 230051, China
*
Corresponding author: Dong Yang; Email: yangdong@ahu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Dual-robot system has been widely applied to the field of handling and palletizing for its high efficiency and large workspace. It is one of the key problems of the trajectory planning to determine the collision avoidance method of the dual-robot system. In the present study, a collision avoidance trajectory planning method for the dual-robot system was proposed on the basis of a modified artificial potential field (APF) algorithm. The interference and collision criterion of the dual-robot system was given firstly, which was established based on the method of kinematic analysis in robotics. And then, in consideration of the problem of excessive virtual potential field force induced by using the traditional APF algorithm in the process of dual-robot trajectory planning, a modified APF algorithm was proposed. Finally, the modified APF algorithm was used for motion control of a dual-robot palletizing process, and the collision avoidance performance of the proposed collision avoidance algorithm was studied through a dual-robot palletizing simulation and experiment. The results have shown that with the proposed collision avoidance trajectory planning algorithm, the two robots in dual-robot system can maintain a safe distance at all times during palletizing process. Compared with the traditional APF and rapidly-exploring random tree (RRT) algorithm, the trajectory solution time of the modified APF algorithm is greatly reduced. And the modified APF algorithm’s convergence time is 14.2% shorter than that of the traditional APF algorithm.

Keywords

dual-robot systemcollision detectingtrajectory planningcollision avoidancemodified APF
Type
Research Article
Information
Robotica , Volume 42 , Issue 3 , March 2024 , pp. 846 - 863
Copyright
© The Author(s), 2024. 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.)

References

Xiao, L., Zhang, Y., Liao, B., Zhang, Z., Ding, L. and Jin, L., “A velocity-level bi-criteria optimization scheme for coordinated path tracking of dual robot manipulators using recurrent neural network,” Front. Neurorobot. 11, 47 (2017).Google Scholar
Yagiz, N., Hacioglu, Y. and Arslan, Y. Z., “Load transportation by dual-arm robot using sliding mode control,” J. Mech. Sci. Technol. 24(5), 1177–1184 (2010).Google Scholar
Wang, Z., Gan, Y. and Dai, X., “A novel task-oriented framework for dual-arm robotic assembly task,” Front. Mech. Eng. 16(3), 528–545 (2021).Google Scholar
Xiong, J., Fu, Z., Chen, H., Pan, J., Gao, X. and Chen, X., “Simulation and trajectory generation of dual-robot collaborative welding for intersecting pipes,” Int. J. Adv. Manuf. Technol. 111(7-8), 2231–2241 (2020).CrossRefGoogle Scholar
Yoon, H. J., Chung, S. Y. and Hwang, M. J., “Shadow space modeling and task planning for collision-free cooperation of dual manipulators for planar task,” Int. J. Control. Autom. Syst. 17(4), 995–1006 (2019).Google Scholar
Zhang, Z., Zheng, L., Chen, Z., Kong, L. and Hamid, R. K., “Mutual-collision-avoidance scheme synthesized by neural networks for dual redundant robot manipulators executing cooperative tasks,” IEEE Trans. Neural. Netw. Learn. Syst. 32(3), 1052–1066 (2020).Google Scholar
Shin, K. G. and Zheng, Q., “Minimum-time collision-free trajectory planning for dual-robot systems,” IEEE Trans. Robot. Autom. 8(5), 641–644 (1992).Google Scholar
Chang, C., Chung, M. and Lee, B., “Collision avoidance of two general robot manipulators by minimum delay time,” IEEE Trans. Syst. Man. Cybern. 24(3), 517–522 (1994).CrossRefGoogle Scholar
Ju, M., Liu, J. and Hwang, K., “Real-time velocity alteration strategy for collision-free trajectory planning of two articulated robot manipulators,” J. Intell. Robot. Syst. 33(2), 167–186 (2002).Google Scholar
Chen, Y. and Li, L., “Collision-free trajectory planning for dual-robot systems using B-splines,” Int. J. Adv. Robot. Syst. 14(4), 1–10 (2017).Google Scholar
Liang, J., Xu, Z., Zhou, X., Li, S. and Ye, G., “Recurrent neural networks based collision-free motion planning for dual manipulators under multiple constraints,” IEEE Access 8, 54225–54236 (2020).Google Scholar
Liu, Y., Yu, C., Sheng, J. and Zhang, T., “Self-collision avoidance trajectory planning and robust control of a dual-arm space robot,” Int. J. Control Autom. Syst. 16(6), 2896–2905 (2018).Google Scholar
Su, C. and Xu, J., “A novel non-collision path planning strategy for multi-manipulator cooperative manufacturing systems,” Int. J. Adv. Manuf. Technol. 120(5-6), 3299–3324 (2022).Google Scholar
Zhao, H., Zhao, J., Duan, X., Gong, X. and Bian, G., “Research on collision avoidance control of multi-arm medical robot based on C-Space,” IEEE Access 8, 93219–93229 (2020).Google Scholar
Lozano-Perez, T., “A simple motion-planning algorithm for general robot manipulators,” IEEE J. Robot. Autom. 3(3), 224–238 (1987).Google Scholar
Masehian, E. and Amin-Naseri, M. R., “A voronoi diagram-visibility graph-potential field compound algorithm for robot path planning,” J. Robot. Syst. 21(6), 275–300 (2004).Google Scholar
Ferguson, D. and Stentz, A., “Using interpolation to improve path planning: The field D* algorithm,” J. Field Robot. 23(2), 79–101 (2006).Google Scholar
Duchon, F., Andrej, B., Martin, K., Peter, B., Martin, F., Tomas, F. and Ladislav, J., “Path planning with modified a star algorithm for a mobile robot,” Proc. Eng. 96, 59–69 (2014).Google Scholar
Merchan-Cruz, E. A. and Morris, A. S., “Fuzzy-GA-based trajectory planner for robot manipulators sharing a common workspace,” IEEE Trans. Robot. 22(4), 613–624 (2006).CrossRefGoogle Scholar
Gomez-Bravo, F., Carbone, G. and Fortes, J. C., “Collision-free trajectory planning for hybrid manipulators,” Mechatronics 22(6), 836–851 (2012).Google Scholar
Abu-Dakka, F. J., Valero, F. J., Suñer, J. L. and Mata, V., “A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments,” Robotica 33(03), 669–683 (2015).Google Scholar
Yang, H., Qi, J., Miao, Y., Sun, H. and Li, J., “A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization,” IEEE Trans. Ind. Electron. 66(11), 8557–8566 (2019).Google Scholar
Xu, Z., Zhou, X. and Li, S., “Deep recurrent neural networks based obstacle avoidance control for redundant manipulators,” Front. Neurorobot. 13, 47 (2019).Google Scholar
Ameer, H. K., Li, S. and Luo, X., “Obstacle avoidance and tracking control of redundant robotic manipulator: An RNN-based metaheuristic approach,” IEEE Trans. Ind. Inform. 16(7), 4670–4680 (2020).Google Scholar
Fethi, M., Boumedyen, B. and Mohamed, N. A., “Distributed path planning of a multi-robot system based on the neighborhood APF approach,” Simul. Trans. Soc. Mod. Simul. 95(7), 637–657 (2018).Google Scholar
Khatib, O., “Real-time obstacle avoidance system for manipulators and mobile robots,” Int. J. Robot. Res. 5(1), 90–98 (1986).Google Scholar
Wang, W., Zhu, M., Wang, X., He, S., He, J. and Xu, Z., “An improved APF method of trajectory planning and obstacle avoidance for redundant manipulators,” Int. J. Adv. Robot. Syst. 15(5), 1–13 (2018).Google Scholar
Anoush, S. and Amirreza, M. M., “A motion planning algorithm for redundant manipulators using rapidly exploring randomized trees and APFs,” IEEE Access 9, 26059–26070 (2021).Google Scholar
Zhou, H., Zhou, S., Yu, J., Zhang, Z. and Liu, Z., “Trajectory optimization of pickup manipulator in obstacle environment based on improved APF method,” Appl. Sci. Basel 10(3), 935 (2020).Google Scholar
Park, S., Lee, M. and Kim, J., “Trajectory planning with collision avoidance for redundant robots using Jacobian and APF-based real-time inverse kinematics,” Int. J. Control. Autom. Syst. 18(8), 2095–2107 (2020).Google Scholar
Zhang, H., Zhu, Y., Liu, X. and Xu, X., “Analysis of obstacle avoidance strategy for dual-arm robot based on speed field with improved APF algorithm,” Electronics 10(15), 1850 (2021).Google Scholar
Xu, T., Zhou, H., Tan, S., Li, Z., Fu, X. and Peng, Y., “Mechanical arm obstacle avoidance path planning based on improved APF method,” Ind. Robot. 49(2), 271–279 (2021).Google Scholar
Tang, Z., Xu, L., Wang, Y., Kang, Z. and Xie, H., “Collision-free motion planning of a six-link manipulator used in a citrus picking robot,” Appl. Sci. Basel 11(23), 11336 (2022).Google Scholar
Liu, Y., Xiao, F., Tong, X., Tao, B., Xu, M., Jiang, J., Chen, B., Cao, Y. and Sun, N., “Manipulator trajectory planning based on work subspace division,” Concurr. Comput. Pract. Exp. 34(5), 6710 (2022).Google Scholar
Kosuge, K., Ishikawa, J., Furuta, K. and Sakai, M., “Control of Single-Master Multi-Slave Manipulator System Using VIM,” In: IEEE International Conference on Robotics & Automation (ICRA) (1990).Google Scholar
Khatib, O., “Real-time obstacle avoidance system for manipulators and mobile robots,” Int. J. Robot. Res. 5(1), 90–98 (1986).Google Scholar
Yang, W., Wu, P., Zhou, X., Lv, H., Liu, X., Zhang, G., Hou, Z. and Wang, W., “Improved APF and dynamic window method for amphibious robot fish path planning,” Appl. Sci. Basel 11, 2114 (2021).Google Scholar
Ryo, K. and Takeshi, N., “Motion planning by T-RRT with potential function for vertical articulated robots,” Electr. Eng. Jpn. 204(2), 34–43 (2018).Google Scholar
Kang, J., Lim, D., Choi, Y., Jang, W. and Jung, J., “Improved RRT-connect algorithm based on triangular inequality for robot path planning,” Sensors 21(2), 333 (2021).Google Scholar