Hostname: page-component-74d7c59bfc-b9mx5 Total loading time: 0 Render date: 2026-02-01T10:23:20.746Z Has data issue: false hasContentIssue false
  • English
  • Français

A study on path-planning algorithm for a multi-section continuum robot in confined multi-obstacle environments

Published online by Cambridge University Press:  16 September 2024

Guohua Gao ,
Dongjian Li ,
Kai Liu Open the ORCID record for Kai Liu [Opens in a new window] ,
Yuxin Ge  and
Chunxu Song
Guohua Gao
Affiliation:
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China
Dongjian Li
Affiliation:
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China
Kai Liu*
Affiliation:
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China
Yuxin Ge
Affiliation:
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China
Chunxu Song
Affiliation:
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China
*
Corresponding author: Kai Liu; Email: kailiu@bjut.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

In confined multi-obstacle environments, generating feasible paths for continuum robots is challenging due to the need to avoid obstacles while considering the kinematic limitations of the robot. This paper deals with the path-planning algorithm for continuum robots in confined multi-obstacle environments to prevent their over-deformation. By modifying the tree expansion process of the Rapidly-exploring Random Tree Star (RRT*) algorithm, a path-planning algorithm called the continuum-RRT* algorithm herein is proposed to achieve fewer iterations and faster convergence as well as generating desired paths that adhere to the kinematic limitations of the continuum robots. Then path planning and path tracking are implemented on a tendon-driven four-section continuum robot to validate the effectiveness of the path-planning algorithm. The path-planning results show that the path generated by the algorithm indeed has fewer transitions, and the path generated by the algorithm is closer to the optimal path that satisfies the kinematic limitations of the continuum robot. Furthermore, path-tracking experiments validate the successful navigation of the continuum robot along the algorithm-generated path, exhibiting an error range of 2.51%–3.91%. This attests to the effectiveness of the proposed algorithm in meeting the navigation requirements of continuum robots.

Keywords

continuum robotpath planningpath-tracking motionRRT* algorithmnavigation

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 10 , October 2024 , pp. 3324 - 3347
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.)

Article purchase

Temporarily unavailable

References

Luca, A. D., Oriolo, G. and Vendittelli, M., “Control of Wheeled Mobile Robots: An Experimental Overview,” In: Articulated and Mobile Robots for Services and Technology Nicosia, B. S., Bicchi, A. and Valigi, P., eds.) (Springer-Verlag, U.K., 2001) pp. 181–223.Google Scholar
Rubio, F., Valero, F. and Llopis-Albert, C., “A review of mobile robots: Concepts, methods, theoretical framework, and applications,” Int J Adv Robot Syst 16(2), 1729881419839596 (2019).CrossRefGoogle Scholar
Ding, N., Peng, C., Lin, M. and Wu, C., “A comprehensive review on automatic mobile robots: Applications, perception, communication and control,” J Circuits Syst Comput 31(08), 2250153 (2022).CrossRefGoogle Scholar
Walker, I. D., “Continuous backbone “continuum” robot manipulators,” Int Scholar Res Notice 2023(1), 726506 (2013).Google Scholar
Kim, S., Laschi, C. and Trimmer, B., “Soft robotics: A bioinspired evolution in robotics,” Trends Biotechnol 31(5), 287–294 (2013).CrossRefGoogle ScholarPubMed
Hopkins, J. K., Spranklin, B. W. and Gupta, S. K., “A survey of snake-inspired robot designs,” Bioinspir Biomim 4(2), 021001 (2009).CrossRefGoogle ScholarPubMed
Yang, C., Geng, S., Walker, I., Branson, D. T., Liu, J., Dai, J. S. and Kang, R., “Geometric constraint-based modeling and analysis of a novel continuum robot with shape memory alloy initiated variable stiffness,” Int J Robot Res 39(14), 1620–1634 (2020).CrossRefGoogle Scholar
Burgner-Kahrs, J., Rucker, D. C. and Choset, H., “Continuum robots for medical applications: A survey,” IEEE Trans Robot 31(6), 1261–1280 (2015).CrossRefGoogle Scholar
Shi, C., Luo, X., Qi, P., Li, T., Song, S., Najdovski, Z., Fukuda, T. and Ren, H., “Shape sensing techniques for continuum robots in minimally invasive surgery: A survey,” IEEE Trans Bio Med Eng 64(8), 1665–1678 (2016).CrossRefGoogle ScholarPubMed
Dupont, P. E., Simaan, N., Choset, H. and Rucker, C., “Continuum robots for medical interventions,” Proceed IEEE 110(7), 847–870 (2022).CrossRefGoogle ScholarPubMed
Xu, K., Zhao, J. and Zheng, X., “Configuration comparison among kinematically optimized continuum manipulators for robotic surgeries through a single access port,” Robotica 33(10), 2025–2044 (2015).CrossRefGoogle Scholar
Zhang, R., Xie, D., Qian, C., Duan, X. and Li, C., “Design of a flexible robot toward transbronchial lung biopsy,” Robotica 41(3), 1055–1065 (2023).CrossRefGoogle Scholar
Angrisani, L., Grazioso, S., Gironimo, G. D., Panariello, D. and Tedesco, A., “On the Use of Soft Continuum Robots for Remote Measurement Tasks in Constrained Environments: A Brief Overview of Applications,” In: IEEE International Symposium on Measurements & Networking (M& N), (IEEE, 2019) pp. 1–5.CrossRefGoogle Scholar
Kolachalama, S. and Lakshmanan, S., “Continuum robots for manipulation applications: A survey,” J Robot 2020(1), 4187048 (2020).Google Scholar
Wang, P., Guo, S., Wang, X. and Wu, Y., “Design and analysis of a novel variable stiffness continuum robot with built-in winding-styled ropes,” IEEE Robot Autom Lett 7(3), 6375–6382 (2022).CrossRefGoogle Scholar
Russo, M., Sadati, S. M. H., Dong, X., Mohammad, A., Walker, I. D., Bergeles, C., Xu, K. and Axinte, D. A., “Continuum robots: An overview,” Adv Intell Syst 5(5), 2200367 (2023).CrossRefGoogle Scholar
Qin, G., Ji, A., Cheng, Y., Zhao, W., Pan, H., Shi, S. and Song, Y., “Design and motion control of an under-actuated snake arm maintainer,” Robotica 40(6), 1763–1782 (2022).CrossRefGoogle Scholar
Cobos-Guzman, S., Palmer, D. and Axinte, D., “Kinematic model to control the end-effector of a continuum robot for multi-axis processing,” Robotica 35(1), 224–240 (2017).CrossRefGoogle Scholar
Sarli, N. and Simaan, N., “Minimal Visual Occlusion Redundancy Resolution of Continuum Robots in Confined Spaces,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (IEEE, 2017) pp. 6448–6454.CrossRefGoogle Scholar
Kato, T., Okumura, I., Song, S. E., Golby, A. J. and Hata, N., “Tendon-driven continuum robot for endoscopic surgery: Preclinical development and validation of a tension propagation model,” IEEE/ASME Trans Mechatron 20(5), 2252–2263 (2014).CrossRefGoogle Scholar
Lai, J., Huang, K., Lu, B., Zhao, Q. and Chu, H. K., “Verticalized-tip trajectory tracking of a 3D-printable soft continuum robot: Enabling surgical blood suction automation,” IEEE/ASME Trans Mechatron 27(3), 1545–1556 (2021).CrossRefGoogle Scholar
Dupourqué, L., Masaki, F., Colson, Y. L., Kato, T. and Hata, N., “Transbronchial biopsy catheter enhanced by a multisection continuum robot with follow-the-leader motion,” Int J Comput Assist Radiol Surg 14(11), 2021–2029 (2019).CrossRefGoogle ScholarPubMed
Dong, X., Axinte, D., Palmer, D., Cobos, S., Raffles, M., Rabani, A. and Kell, J., “Development of a slender continuum robotic system for on-wing inspection/repair of gas turbine engines,” Robot Com Int Manuf 44, 218–229 (2017).CrossRefGoogle Scholar
Hawkes, E. W., Blumenschein, L. H., Greer, J. D. and Okanura, A. M., “A soft robot that navigates its environment through growth,” Sci Robot 2(8), eaan3028 (2017).CrossRefGoogle ScholarPubMed
Phillips, B. T., Becker, K. P., Kurumaya, S., Galloway, K. C., Whittredge, G., Vogt, D. M., Teeple, C. B., Rosen, M. H., Pieribone, V. A., Gruber, D. F. and Wood, R. J., “A dexterous, glove-based teleoperable low-power soft robotic arm for delicate deep-sea biological exploration,” Sci Rep 8(1), 14779 (2018).CrossRefGoogle ScholarPubMed
Rasheed, A. A. A., Al-Araji, A. S. and Abdullah, M. N., “Static and dynamic path planning algorithms design for a wheeled mobile robot based on a hybrid technique,” Int J Intell Eng Syst 15(5), 167–181 (2022).Google Scholar
Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T. and Jurišica, L., “Path planning with modified a star algorithm for a mobile robot,” Procedia Engineer 96, 59–69 (2014).CrossRefGoogle Scholar
Elbanhawi, M. and Simic, M., “Sampling-based robot motion planning: A review,” IEEE Access 2, 56–77 (2014).CrossRefGoogle Scholar
Park, M. G., Jeon, J. H. and Lee, M. C., “Obstacle Avoidance for Mobile Robots using Artificial Potential Field Approach with Simulated Annealing,” In: IEEE International Symposium on Industrial Electronics Proceedings, (IEEE, 2001) pp. 1530–1535.Google Scholar
Palmer, D., Cobos-Guzman, S. and Axinte, D., “Real-time method for tip following navigation of continuum snake arm robots,” Robot Auton Syst 62(10), 1478–1485 (2014).CrossRefGoogle Scholar
Godage, I. S., Branson, D. T., Guglielmino, E. and Caldwell, D. G., “Path Planning for Multisection Continuum Arms,” In: IEEE International Conference on Mechatronics and Automation, (IEEE, 2012) pp. 1208–1213.CrossRefGoogle Scholar
Ataka, A., Qi, P., Liu, H. and Althoefer, K., “Real-Time Planner for Multi-Segment Continuum Manipulator in Dynamic Environments,” In: IEEE International Conference on Robotics and Automation (ICRA), (IEEE, 2016) pp. 4080–4085.CrossRefGoogle Scholar
Ouyang, B., Liu, Y., Tam, H.-Y. and Sun, D., “Design of an interactive control system for a multisection continuum robot,” IEEE/ASME Trans Mechatron 23(5), 2379–2389 (2018).CrossRefGoogle Scholar
Wu, H. R., Yu, J. J., Pan, J. and Xu, P., “A novel obstacle avoidance heuristic algorithm of continuum robot based on FABRIK,” Sci China Technol Sci 65(12), 2952–2966 (2022).CrossRefGoogle Scholar
Torres, L. G., Kuntz, A., Gilbert, H. B., Swaney, P. J., Hendrick, R. J., Webster, R. J. III and Alterovit, R., “A Motion Planning Approach to Automatic Obstacle Avoidance During Concentric Tube Robot Teleoperation,” In: IEEE international conference on robotics and automation (ICRA), (IEEE, 2015) pp. 2361–2367.CrossRefGoogle Scholar
Greigarn, T., Poirot, N. L., Xu, X. and Cavusoglu, M. C., “Jacobian-based task-space motion planning for MRI-actuated continuum robots,” IEEE Robot Autom Lett 4(1), 145–152 (2018).CrossRefGoogle ScholarPubMed
Seleem, I. A., Assal, S. F. M., Ishii, H. and El-Hussieny, H., “Demonstration-guided pose planning and tracking for multi-section continuum robots considering robot dynamics,” IEEE Access 7, 166690–166703 (2019).CrossRefGoogle Scholar
Meng, B. H., Godage, I. S. and Kanj, I., “RRT*-based path planning for continuum arms,” IEEE Robot Autom Lett 7(3), 6830–6837 (2022).CrossRefGoogle Scholar
Aggarwal, S. and Kumar, N., “Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges,” Comput Commun 149, 270–299 (2020).CrossRefGoogle Scholar
Zhao, Z. Y., Zhou, M. C. and Liu, S. X., “Iterated greedy algorithms for flow-shop scheduling problems: A tutorial,” IEEE Trans Autom Sci Eng 19(3), 1941–1959 (2021).CrossRefGoogle Scholar
Palmer, D. and Axinte, D., “Active uncoiling and feeding of a continuum arm robot,” Robot Com Int Manuf 56, 107–116 (2019).CrossRefGoogle Scholar
Webster, R. J. III and Jones, B. A., “Design and kinematic modeling of constant curvature continuum robots: A review,” Int J Robot Res 29(13), 1661–1683 (2010).CrossRefGoogle Scholar
Choset, H. and Henning, W., “A follow-the-leader approach to serpentine robot motion planning,” J Aerospace Eng 12(2), 65–73 (1999).CrossRefGoogle Scholar
Elbanhawi, M., Simic, M. and Jazar, R. N., “Continuous path smoothing for car-like robots using B-spline curves,” J Intell Robot Syst 80(S1), 23–56 (2015).CrossRefGoogle Scholar