Adaptive path planning of surgical robots based on environmental attributes

FANG Cheng; HU Zhi

doi:10.12299/jsues.24-0063

Volume 39 Issue 2

Jun. 2025

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FANG Cheng, HU Zhi. Adaptive path planning of surgical robots based on environmental attributes[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 216-222. doi: 10.12299/jsues.24-0063

Citation:

FANG Cheng, HU Zhi. Adaptive path planning of surgical robots based on environmental attributes[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 216-222. doi: 10.12299/jsues.24-0063

Citation:

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Adaptive path planning of surgical robots based on environmental attributes

doi: 10.12299/jsues.24-0063

FANG Cheng,
HU Zhi^,

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2024-03-28
Available Online: 2025-09-30
Publish Date: 2025-06-30

Abstract

Abstract

In a dual-arm surgical robot environment, it is essential for the robotic arms to reach target positions safely and efficiently. An adaptive APF-Informed-RRT* path planning algorithm was proposed to overcome limitation of traditional APF-Informed-RRT* algorithms, such as lack of adaptability and insufficient path stability, thereby improving the quality of path planning for robotic arms. The coefficients of the attraction term, repulsion term, and random guidance term were dynamically adjusted based on the Euclidean distance from the robot's current position to the target, as well as the density of obstacles in the nearby area. This adaptability helps to reduce the path planning cost by adjusting to the environment. Simulation results show that compared to the APF-Informed-RRT* algorithm, the proposed method performs better in terms of path cost and search range in a 3D oral simulation environment. This adaptability endows the algorithm with effective stability in path planning tasks.
- surgical robot,
- path planning,
- non-uniform sampling,
- adaptive artificial potential field (APF)

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References(10)

References

[1]	ZHANG X M, YANG F, JIN Q W, et al. Path planning algorithm for dual-arm robot based on depth deterministic gradient strategy algorithm[J] . Mathematics, 2023, 11(20): 4392. doi: 10.3390/math11204392
[2]	LIU D X, ZHANG J C, LI Y. Research on master-slave coordinated control method of dual-arm robot[C] //Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). Singapore: Springer, 2023: 1037−1047.
[3]	KARAMAN S, FRAZZOLI E. Sampling-based algorithms for optimal motion planning[J] . The International Journal of Robotics Research, 2011, 30(7): 846 − 894. doi: 10.1177/0278364911406761
[4]	KARAMAN S, WALTER M R, PEREZ A, et al. Anytime motion planning using the RRT[C] //Proceedings of 2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011: 1478−1483.
[5]	GAMMELL J D, SRINIVASA S S, BARFOOT T D. Informed RRT: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic[C] //Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago: IEEE, 2014: 2997−3004.
[6]	LAVALLE S M, KUFFNER J R. Randomized kinodynamic planning[J] . The International Journal of Robotics Research, 2001, 20(5): 378 − 400. doi: 10.1177/02783640122067453
[7]	NADERI K, RAJAMÄKI J, HÄMÄLÄINEN P. RT-RRT*: a real-time path planning algorithm based on RRT[C] //Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games. Paris: ACM, 2015: 113−118.
[8]	CHEN J G, ZHAO Y, XU X. Improved RRT-connect based path planning algorithm for mobile robots[J] . IEEE Access, 2021, 9: 145988 − 145999. doi: 10.1109/ACCESS.2021.3123622
[9]	TIAN Z Y, GUO C, LIU Y, et al. An improved RRT robot autonomous exploration and SLAM construction method[C] //Proceedings of 2020 5th International Conference on Automation, Control and Robotics Engineering. Dalian: IEEE, 2020: 612−619.
[10]	ZHAO P L, CHANG Y H, WU W K, et al. Dynamic RRT: fast feasible path planning in randomly distributed obstacle environments[J] . Journal of Intelligent & Robotic Systems, 2023, 107(4): 48.