Hostname: page-component-89b8bd64d-r6c6k Total loading time: 0 Render date: 2026-05-08T14:17:11.971Z Has data issue: false hasContentIssue false
  • English
  • Français

FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

Published online by Cambridge University Press:  27 December 2023

Guangzhou Xiao Open the ORCID record for Guangzhou Xiao [Opens in a new window] ,
Lixian Zhang ,
Tong Wu ,
Yuejiang Han ,
Yihang Ding  and
Chengzhe Han
Guangzhou Xiao
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Lixian Zhang*
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Tong Wu
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Yuejiang Han
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Yihang Ding
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Chengzhe Han
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
*
Corresponding author: Lixian Zhang; Email: lixianzhang@hit.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper is concerned with the problem of collision-free path planning for manipulators in multi-obstacle scenarios. Aiming at overcoming the deficiencies of existing algorithms in excessive time consumption and poor expansion quality, a path planning algorithm named Fast Bi-directional Rapidly-exploring Random Tree (FBi-RRT) with novel heuristic node expansion is proposed, which includes a selective-expansion strategy and a vertical-exploration strategy. The selective-expansion strategy is designed to guide the selection of the nearest-neighbor node to avoid the repeated expansion failure, thereby shortening the overall planning time. Also, the vertical-exploration strategy is developed to regulate the expansion direction of the collision nodes to escape from the obstacle space with less blindness, thus improving the expansion quality and further reducing time cost. Compared with previous planning algorithms, FBi-RRT can generate a feasible path for manipulators in a drastically shorter time. To validate the effectiveness of the proposed heuristic node expansion, FBi-RRT is conducted on a 6-DOF manipulator and tested in five scenarios. The experimental results demonstrate that FBi-RRT outperforms the existing methods in time consumption and expansion quality.

Keywords

path planningrapidly exploring random treerobotic manipulatorsampling-based algorithmtime consumption

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 3 , March 2024 , pp. 644 - 659
Copyright
© The Author(s), 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

Kim, Y., Genevriere, E., Harker, P., Choe, J., Balicki, M., Regenhardt, R. W., Justin, E. V., Adam, A. D., Aman, B. P. and Zhao, X., “Telerobotic neurovascular interventions with magnetic manipulation,” Sci. Robot. 7(65), 1–20 (2022).Google Scholar
Hu, Y., Li, J., Chen, Y., Wang, Q., Chi, C., Zhang, H., Gao, Q., Lan, Y., Li, Z., Mu, Z., Sun, Z. and Knoll, A., “Design and control of a highly redundant rigid-flexible coupling robot to assist the COVID-19 oropharyngeal-swab sampling,” IEEE Robot. Autom. Lett. 7(2), 1856–1863 (2021).Google Scholar
Dong, H., Feng, Y., Qiu, C. and Chen, I. M., “Construction of interaction parallel manipulator: Towards rehabilitation massage,” IEEE/ASME Trans. Mechatron. 28(1), 372–384 (2022).Google Scholar
Liu, Y., Du, Z., Wu, Z., Liu, F. and Li, X., “Multiobjective preimpact trajectory planning of space manipulator for self-assembling a heavy pay-load,” Int. J. Adv. Robot. Syst. 18(1), 1–13 (2021).Google Scholar
Da Fonseca, M., Goes, C. S., Seito, N., da Silva Duarte, K. and de Oliveira, J., “Attitude dynamics and control of a spacecraft like a robotic manipulator when implementing on-orbit servicing,” Acta Astronaut. 137, 490–497 (2017).Google Scholar
Peng, J., Xu, W., Liu, T., Yuan, H. and Liang, B., “End-effector pose and arm-shape synchronous planning methods of a hyper-redundant manipulator for spacecraft repairing,” Mech. Mach. Theory 155(104062), 1–25 (2021).Google Scholar
Cao, X., Zou, X., Jia, C., Chen, M. and Zeng, Z., “RRT-based path planning for an intelligent litchi-picking manipulator,” Comput. Electron. Agric. 156, 105–118 (2019).Google Scholar
Mehta, S. S. and Burks, T. F., “Vision-based control of robotic manipulator for citrus harvesting,” Comput. Electron. Agric. 102, 146–158 (2014).Google Scholar
Lauretti, C., Grasso, T., de Marchi, E., Grazioso, S. and di Gironimo, G., “A geometric approach to inverse kinematics of hyper-redundant manipulators for tokamaks maintenance,” Mech. Mach. Theory 176(104967), 1–25 (2022).Google Scholar
Lee, J. K., Lee, H. J., Park, B. S. and Kim, K., “Bridge-transported bilateral master-slave servo manipulator system for remote manipulation in spent nuclear fuel processing plant,” J. Field Robot. 29(1), 138–160 (2012).Google Scholar
Hourtash, A. and Tarokh, M., “Manipulator path planning by decom-position: Algorithm and analysis,” IEEE Trans. Robot. Autom. 17(6), 842–856 (2001).Google Scholar
Li, Z., Li, C., Li, S. and Cao, X., “A fault-tolerant method for motion planning of industrial redundant manipulator,” IEEE Trans. Ind. Inform. 16(12), 7469–7478 (2020).Google Scholar
Xiao, G., Wu, T., Weng, R., Zhang, R., Han, Y., Dong, Y. and Liang, Y., “NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion,” Sci. China-Technol. Sci. 66(5), 1205–1213 (2023).Google Scholar
Tao, X., Lang, N., Li, H. and Xu, D., “Path planning in uncertain environment with moving obstacles using warm start cross entropy,” IEEE/ASME Trans. Mechatron. 27(2), 800–810 (2022).Google Scholar
Jiang, L., Liu, S., Cui, Y. and Jiang, H., “Path planning for robotic manipulator in complex multi-obstacle environment based on improved_RRT,” IEEE/ASME Trans. Mechatron. 27(6), 4774–4785 (2022).Google Scholar
González, D., Pérez, J., Milanés, V. and Nashashibi, F., “A review of motion planning techniques for automated vehicles,” IEEE Trans. Intell. Transp. Syst. 17(4), 1135–1145 (2016).Google Scholar
Li, B. and Chen, B., “An adaptive rapidly-exploring random tree,” IEEE/CAA J. Automatica Sin. 9(2), 283–294 (2022).Google Scholar
Gammell, J. D., Barfoot, T. D. and Srinivasa, S. S., “Informed sampling for asymptotically optimal path planning,” IEEE Trans. Robot. 34(4), 966–984 (2018).Google Scholar
Pardi, T., Ortenzi, V., Fairbairn, C., Pipe, T., Esfahani, A. M. G. and Stolkin, R., “Planning maximum-manipulability cutting paths,” IEEE Robot. Autom. Lett. 5(2), 1999–2006 (2020).Google Scholar
Kleinbort, M., Solovey, K., Littlefield, Z., Bekris, K. E. and Halperin, D., “Probabilistic completeness of RRT for geometric and kinodynamic planning with forward propagation,” IEEE Robot. Autom. Lett. 4(2), x–xvi (2019).Google Scholar
Thakar, S., Rajendran, P., Kim, H., Kabir, A. M. and Gupta, S. K., “Accelerating Bi-Directional Sampling-based Search for Motion Planning of Non-Holonomic Mobile Manipulators,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2020) pp. 6711–6717.Google Scholar
Thakar, S., Rajendran, P., Kabir, A. M. and Gupta, S. K., “Manipulator motion planning for part pickup and transport operations from a moving base,” IEEE Trans. Autom. Sci. Eng. 19(1), 191–206 (2022).Google Scholar
Chu, X., Hu, Q. and Zhang, J., “Path planning and collision avoidance for a multi-arm space maneuverable robot,” IEEE Trans. Aerosp. Electron. Syst. 54(1), 217–232 (2018).Google Scholar