Snake-inspired trajectory planning and control for confined pipeline inspection with hyper-redundant manipulators

Junjie Zhu , Mingming Su , Longchuan Li , Yuxuan Xiang , Jianming Wang , Xuan Xiao

Biomimetic Intelligence and Robotics ›› 2025, Vol. 5 ›› Issue (3) : 100245 -100245.

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Biomimetic Intelligence and Robotics ›› 2025, Vol. 5 ›› Issue (3) : 100245 -100245. DOI: 10.1016/j.birob.2025.100245
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Snake-inspired trajectory planning and control for confined pipeline inspection with hyper-redundant manipulators

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Abstract

The hyper-redundant manipulator (HRM) can explore narrow and curved pipelines by leveraging its high flexibility and redundancy. However, planning collision-free motion trajectories for HRMs in confined environments remains a significant challenge. To address this issue, a pipeline inspection approach that combines nonlinear model predictive control (NMPC) with the snake-inspired crawling algorithm(SCA) is proposed. The approach consists of three processes: insertion, inspection, and exit. The insertion and exit processes utilize the SCA, inspired by snake motion, to significantly reduce path planning time. The inspection process employs NMPC to generate collision-free motion. The prototype HRM is developed, and inspection experiments are conducted in various complex pipeline scenarios to validate the effectiveness and feasibility of the proposed method. Experimental results demonstrate that the approach effectively minimizes the computational cost of path planning, offering a practical solution for HRM applications in pipeline inspection.

Keywords

HRM / NMPC / Trajectory planning / Bionics design / Collision avoidance

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Junjie Zhu, Mingming Su, Longchuan Li, Yuxuan Xiang, Jianming Wang, Xuan Xiao. Snake-inspired trajectory planning and control for confined pipeline inspection with hyper-redundant manipulators. Biomimetic Intelligence and Robotics, 2025, 5(3): 100245-100245 DOI:10.1016/j.birob.2025.100245

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CRediT authorship contribution statement

Junjie Zhu: Writing - original draft, Data curation. Mingming Su: Writing - review & editing, Software. Longchuan Li: Writing - review & editing, Methodology. Yuxuan Xiang: Supervision, Investigation. Jianming Wang: Supervision. Xuan Xiao: Writing - review & editing, Supervision, Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.birob.2025.100245.

References

[1]

Z. Mu, L. Zhang, L. Yan, Z. Li, R. Dong, C. Wang, N. Ding, Hyper-redundant manipulators for operations in confined space: Typical applications, key technologies, and grand challenges, IEEE Trans. Aerosp. Electron. Syst. 58(6)(2022) 4928-4937, http://dx.doi.org/10.1109/TAES.2022.3217746.

[2]

Z. Hu, H. Yuan, W. Xu, T. Yang, B. Liang,Equivalent kinematics and pose-configuration planning of segmented hyper-redundant space ma-nipulators, Acta Astronaut. (2021) URL

[3]

W. Wan, C. Sun, J. Yuan, Adaptive caging configuration design algorithm of hyper-redundant manipulator for dysfunctional satellite pre-capture, IEEE Access 8 (2020) 22546-22559, URL

[4]

Q. Luo, Q. Hu, Y. Zhang, Y. Sun, Segmented hybrid motion-force con-trol for a hyper-redundant space manipulator, Aerosp. Sci. Technol. 131 (2022) 107981, http://dx.doi.org/10.1016/j.ast.2022.107981 URL https://www.sciencedirect.com/science/article/pii/S1270963822006551.

[5]

L. Du, J. Yuan, S. Bao, R. Guan, W. Wan, Robotic replacement for disc cutters in tunnel boring machines, Autom. Constr. 140 (2022) 104369, http://dx.doi.org/10.1016/j.autcon.2022.104369, URL https://www.sciencedirect.com/science/article/pii/S0926580522002424.

[6]

L. Zhang, S. Huang, Z. Du, G. Ouyang, H. Chen, Motion-planning algorithm for a hyper-redundant manipulator in narrow spaces, Comput. Mater. Contin. (2022) URL https://api.semanticscholar.org/CorpusID:248341812.

[7]

C. Wang, H. Xie, H. Yang, An iterative path-following method for hyper-redundant snake-like manipulator with joint limits, Ind. Robot 50 (2023) 505-519.

[8]

H. Ji, H. Xie, H. Yang, A spatial path following method for hyper-redundant manipulators by step-by-step search and calculating, in: 2022 7th Interna-tional Conference on Robotics and Automation Engineering, ICRAE, 2022, pp. 292-298, http://dx.doi.org/10.1109/ICRAE56463.2022.10056161.

[9]

A. Soltani, H. Tawfik, J. Goulermas, T. Fernando, Path planning in con-struction sites: performance evaluation of the dijkstra, a, and GA search algorithms, Adv. Eng. Informatics 16 (4) (2002) 291-303, http://dx.doi.org/10.1016/S1474-0346(03)00018-1, URL https://www.sciencedirect.com/science/article/pii/S1474034603000181.

[10]

C. Xu, Z. Liu, C. Hu, X. Li, Improved hybrid A* algorithm obstacle avoid-ance strategy based on reinforcement learning, in: 2023 42nd Chinese Control Conference, CCC, 2023, pp. 4077-4082, http://dx.doi.org/10.23919/CCC58697.2023.10239886.

[11]

R. Song, Y. Liu, R. Bucknall, Smoothed A* algorithm for practical unmanned surface vehicle path planning, Appl. Ocean Res. 83 (2019) 9-20, http://dx.doi.org/10.1016/j.apor.2018.12.001, URL https://www.sciencedirect.com/science/article/pii/S0141118718302621.

[12]

D. Yu, M.-I. Roh, Method for anti-collision path planning using velocity obstacle and A* algorithms for maritime autonomous surface ship, Int.J. Nav. Archit. Ocean. Eng. 16 ( 2024) 100586, http://dx.doi.org/10.1016/j.ijnaoe.2024.100586 URL https://www.sciencedirect.com/science/article/pii/S2092678224000050.

[13]

X. Tang, H. Zhou, T. Xu, Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method, Robotica 42 (2) (2024) 457-481, http://dx.doi.org/10.1017/S0263574723001546.

[14]

M. Luo, X. Hou, J. Yang, Surface optimal path planning using an extended dijkstra algorithm, IEEE Access 8 (2020) 147827-147838, http://dx.doi.org/10.1109/ACCESS.2020.3015976.

[15]

H. Wei, Y. Zheng, G. Gu, RRT-based path planning for follow-the-leader motion of hyper-redundant manipulators, in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2021, pp. 3198-3204, http://dx.doi.org/10.1109/IROS51168.2021.9635876.

[16]

H. Shen, W.-F. Xie, J. Tang, T. Zhou, Adaptive manipulability-based path planning strategy for industrial robot manipulators, IEEE/ASME Trans. Mechatronics 28 (3) (2023) 1742-1753, http://dx.doi.org/10.1109/TMECH.2022.3231467.

[17]

H. Ji, H. Xie, C. Wang, H. Yang, E-RRT*: Path planning for hyper-redundant manipulators, IEEE Robot. Autom. Lett. 8 (12) (2023) 8128-8135, http://dx.doi.org/10.1109/LRA.2023.3325716.

[18]

L. Jia, Y. Huang, T. Chen, Y. Guo, Y. Yin, J. Chen, Mda + rrt: A general ap-proach for resolving the problem of angle constraint for hyper-redundant manipulator, Expert Syst. Appl. 193 (2022) 116379, http://dx.doi.org/10.1016/j.eswa.2021.116379 URL https://www.sciencedirect.com/science/article/pii/S0957417421016717.

[19]

J. Liu, Y. Tong, J. Liu, Review of snake robots in constrained environ-ments, Robot. Auton. Syst. 141 ( 2021) 103785, http://dx.doi.org/10.1016/j.robot.2021.103785 URL https://www.sciencedirect.com/science/article/pii/S0921889021000701.

[20]

G. Qin, A. Ji, Y. Cheng, W. Zhao, H. Pan, S. Shi, Y. Song,A snake-inspired layer-driven continuum robot, Soft Robot. (2021) URL https://api.semanticscholar.org/CorpusID:237608110.

[21]

L. Tang, L.-M. Zhu, X. Zhu, G. Gu, A serpentine curve based motion planning method for cable-driven snake robots, in: 2018 25th International Conference on Mechatronics and Machine Vision in Practice ( M2VIP), 2018, pp. 1-6, http://dx.doi.org/10.1109/M2VIP.2018.8600874.

[22]

X. Hua, G. Wang, J. Xu, K. Chen, Reinforcement learning-based collision-free path planner for redundant robot in narrow duct, J. Intell. Manuf. 32 (2)(2021) 471-482, URL https://EconPapers.repec.org/RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01582-1.

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