Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-10T11:46:09.683Z Has data issue: false hasContentIssue false
  • English
  • Français

Effective motion planning of manipulator based on SDPS-RRTConnect

Published online by Cambridge University Press:  04 October 2021

Junxiang Xu Open the ORCID record for Junxiang Xu [Opens in a new window]  and
Jiwu Wang
Junxiang Xu
Affiliation:
School of Mechanical and Electronic Engineering, Beijing Jiaotong University, Beijing, Haidian District, China
Jiwu Wang*
Affiliation:
School of Mechanical and Electronic Engineering, Beijing Jiaotong University, Beijing, Haidian District, China
*
*Corresponding author. E-mails: 19121251@bjtu.edu.cn, jwwang@bjtu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

In order to improve the speed of motion planning, this paper proposes an improved RRTConnect algorithm (SDPS-RRTConnect) based on sparse dead point saved strategy. Combining sparse expansion strategy and dead point saved strategy, the algorithm can reduce the number of collision detection, fast convergence, avoid falling into local minimum, and ensure the completeness of search space. The algorithm is simulated in different environments. The results show that in complex environments, the sparse dead point saved strategy plays a good effect. In simple environments, the greedy connection strategy works well. Compared with the standard RRT algorithm, the standard RRTConnect algorithm, and the SDPS-RRT algorithm, the SDPS-RRTConnect algorithm has the shortest planning time, and it is suitable for both simple and complex environments. The 500 experiments show that the algorithm has strong robustness. The actual robot experiments show that the path planned by SDPS-RRTConnect algorithm is effective.

Keywords

motion planningRRTRRTConnectcollision detectionsparse dead points saved
Type
Research Article
Information
Robotica , Volume 40 , Issue 6 , June 2022 , pp. 1855 - 1867
Copyright
© The Author(s), 2021. 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

Kim, M., Kim, J., Shin, D. and Jin, M., “Robot-based Shoe Manufacturing System,” In Proceedings of the 18th International Conference on Control, Automation and Systems (ICCAS), Daegwallyeong, Korea, 1491–1494 (2018).Google Scholar
Junge, K., Hughes, J., -uruthel, T. G. and Iida, F., “Improving robotic cooking using batch bayesian optimization,” IEEE Robot. Autom. Lett. 5(2), 760–765 (2020).CrossRefGoogle Scholar
Huang, Y., Zheng, Y., Wang, N., Ota, J. and Zhang, X., “Peg-in-hole assembly based on master-slave coordination for a compliant dual-arm robot,” Assem. Autom. 40(2), 189–198 (2020).CrossRefGoogle Scholar
Wang, Z. and Xiang, X., “Improved Astar Algorithm for Path Planning of Marine Robot. 2018 37th Chinese Control Cpnference(CCC),” 5410–5414 (2018).CrossRefGoogle Scholar
Zhu, D. D. and Sun, J. Q., “A new algorithm based on dijkstra for vehicle path planning considering intersection attribute,” IEEE Access, 19761–19775 (2021).CrossRefGoogle Scholar
Ulises, O. R., Oscar, M. and Roberto, S., “Mobile robot path planning using membrane evolutionary artificial potential field,” Appl. Soft Comput. 77, 236–251 (2019).Google Scholar
Marco, A. C., Victor, A. R. and Uriel, H. B., “Mobile robot path planning using artificial bee colony and evolutionary programming,” Appl. Soft Comput. 30, 319–328 (2015).Google Scholar
Jeong, I. B., Lee, S. J., Kim, J. H. and Quick-RRT*, “Triangular inequality-based implementation of RRT* with improved initial solution and convergence rate,” Expert Syst. Appl. 123, 82–90 (2019).CrossRefGoogle Scholar
Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” Int. J. Robot. Res. 30, 846–894 (2011).CrossRefGoogle Scholar
Nasir, J., Islam, F., Malik, U., Ayaz, Y., Hasan, O., Khan, M. and Muhammad, M. S., “RRT*-SMART: A rapid convergence implementation of RRT,” Int. J. Adv. Robot. Syst. 10, 299 (2013).CrossRefGoogle Scholar
Iram, N., Amna, K. and Zulfiqar, H., “A Comparison of RRT, RRT* and RRT*-Smart Path Planning Algorithms,” Int. J. Comput. Sci. Net. Secur. 16(10), 20–27 (2016).Google Scholar
Weghe, M. V., Ferguson, D. and Srinivasa, S. S., “Randomized path planning for redundant manipulators withoutinverse kinematics,” In Proceedings of the 2007 7th IEEE-RAS International Conference on Humanoid Robots,Pittsburgh, PA, USA, 29 November–1 December, 477–482 (2007).Google Scholar
Lavalle, S. M. and Kuffner, J. J., “Rapidly-Exploring Random Trees: Progress and Prospects,” In Algorithmic & Computational Robotics New Directions; CRC Press: Boca Raton, FL, USA, 293–308 (2000).Google Scholar
Kuffner, J. J. and Lavalle, S. M., “RRT-connect: An efficient approach to single-query path planning,” In Proceedingsof the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, 24–28 April, 995–1001 (2020).Google Scholar
LaValle, S. M. and Kuffner, J. J. Jr, “Randomized kinodynamic planning,” Int. J. Robot. Res. 20, 378–400 (2001).CrossRefGoogle Scholar
Yuan, C., Zhang, W., Liu, G., Pan, X. and Liu, X., “A heuristic rapidly-exploring random trees method for manipulator motion planning,” IEEE Access. 8, 900–910 (2020).CrossRefGoogle Scholar
Wang, X., Luo, X., Han, B., Chen, Y., Liang, G. and Zheng, K., “Collision-free path planning method for robots based on an improved rapidly-exploring random tree algorithm,” Appl. Sci. 10, 1381 (2020).CrossRefGoogle Scholar
Wei, K. and Ren, B. Y., “A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm,” Sensors, 18(2), 571, (2018).CrossRefGoogle Scholar
Wang, X. Y., “Bidirectional Potential Guided RRT* for Motion Planning,” IEEE Access. 7, 95034–95045, (2019).Google Scholar
Zhang, H. J., “Path Planning of Industrial Robot Based on Improved RRT Algorithm in Complex Environments,” IEEE Access. 6, 53296–53306, (2018).CrossRefGoogle Scholar
Kang, G., Kim, Y. B., Lee, Y. H., Oh, H. S., You, W. S. and Choi, H. R., “Sampling-based motion planning of manipulator with goal-oriented sampling,” Intell. Serv. Robot. 12, 265–273 (2019).CrossRefGoogle Scholar
Qiao, W., Fang, Z. and Si, B., “A sampling-based multi-tree fusion algorithm for frontier detection,” Int. J. Adv. Robot. Syst. 16(4), 1729881419865427 (2019).CrossRefGoogle Scholar