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Collision-free path planning for cable-driven continuum robot based on improved artificial potential field

Published online by Cambridge University Press:  05 March 2024

Meng Ding ,
Xianjie Zheng ,
Liaoxue Liu ,
Jian Guo  and
Yu Guo Open the ORCID record for Yu Guo [Opens in a new window]
Meng Ding
Affiliation:
School of Automation, Nanjing University of Science and Technology, Nanjing China
Xianjie Zheng
Affiliation:
School of Automation, Nanjing University of Science and Technology, Nanjing China
Liaoxue Liu
Affiliation:
School of Automation, Nanjing University of Science and Technology, Nanjing China
Jian Guo
Affiliation:
School of Automation, Nanjing University of Science and Technology, Nanjing China
Yu Guo*
Affiliation:
School of Automation, Nanjing University of Science and Technology, Nanjing China
*
Corresponding author: Yu Guo; Email: guoyu@njust.edu.cn
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Abstract

Continuum robot has become a research hotspot due to its excellent dexterity, flexibility and applicability to constrained environments. However, the effective, secure and accurate path planning for the continuum robot remains a challenging issue, for that it is difficult to choose a suitable inverse kinematics solution due to its redundancy in the confined environment. This paper presents a collision-free path planning method based on the improved artificial potential field (APF) for the cable-driven continuum robot, in which the beetle antennae search algorithm is adopted to deal with the optimal problem of APF without the necessary for velocity kinematics. In addition, the local optimum problem of traditional APF is solved by the randomness of the antennae’s direction vector which can make the algorithm easily jump out of local minima. The simulation and experimental results verify the efficiency of the proposed path planning method.

Keywords

continuum robotcollision-free path planningartificial potential fieldbeetle antennae search

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 5 , May 2024 , pp. 1350 - 1367
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

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
Armanini, C., Boyer, F., Mathew, A. T., Duriez, C. and Renda, F., “Soft robots modeling: A structured overview,” IEEE Trans. Rob. 39(3), 1728–1748 (2023).CrossRefGoogle Scholar
Santina, C. D., Duriez, C. and Rus, D., “Model-based control of soft robots: A survey of the state of the art and open challenges,” IEEE Control Syst. Mag. 43(3), 30–65 (2023).CrossRefGoogle Scholar
Yuan, H., Zhou, L. and Xu, W., “A comprehensive static model of cable-driven multi-section continuum robots considering friction effect,” Mech. Mach. Theory 135, 130–149 (2019).CrossRefGoogle Scholar
Buckingham, R. and Graham, A., “Nuclear snake-arm robots,” Ind. Robot 39(1), 6–11 (2012).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,” IEEE International Symposium on Measurements & Networking (M&N), 2019, Catania, Italy (2019) pp. 1–5.Google Scholar
Dupont, P. E., Simaan, N., Choset, H. and Rucker, C., “Continuum robots for medical interventions,” Proc. IEEE 110(7), 847–870 (2022).CrossRefGoogle ScholarPubMed
Hu, X., Chen, A., Luo, Y., Zhang, C. and Zhang, E., “Steerable catheters for minimally invasive surgery: A review and future directions,” Comput. Assist. Surg. 23(1), 21–41 (2018).CrossRefGoogle ScholarPubMed
Ansari, Y., Manti, M., Falotico, E., Cianchetti, M. and Laschi, C., “Multiobjective optimization for stiffness and position control in a soft robot arm module,” IEEE Robot. Autom. Lett. 3(1), 108–115 (2017).CrossRefGoogle Scholar
Tonapi, M. M., Godage, I. S., Vijaykumar, A. M. and D.Walker, I., “A novel continuum robotic cable aimed at applications in space,” Adv. Robot. 29(13), 861–875 (2015).CrossRefGoogle Scholar
Peng, J., Xu, W., Liu, T., Yuan, H. and Liang, B., “End-effector pose and armshape synchronous planning methods of a hyper-redundant manipulator for spacecraft repairing,” Mech. Mach. Theory 155, 104062 (2021).CrossRefGoogle Scholar
Memar, A. H. and Keshmiri, K. T. M.. “Motion Planning for a Multi-segment Continuum robot,” Proceedings of the International Conference on Mechanical Engineering and Mechatronics, Ottawa, Ontario, Canada (2012) pp. 209-1–209-9.Google Scholar
Meng, B. H., Godage, I. S. and Kanj, I., “RRT*-based path planning for pontinuum arms,” IEEE Robot. Autom. Lett. 7(3), 6830–6837 (2022).CrossRefGoogle Scholar
Lai, J., Lu, B., Zhao, Q. and Chu, H. K., “Constrained motion planning of a cable-driven soft robot with compressible curvature modeling,” IEEE Robot. Autom. Lett. 7(2), 4813–4820 (2022).CrossRefGoogle Scholar
Niu, G., Zheng, Z. and Gao, Q.. “Collision Free Path Planning based on Region Clipping for Aircraft Fuel Tank Inspection Robot,” 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014, Hong Kong, China (2014) pp. 3227–3233.Google Scholar
Ananthanarayanan, H. and Ordóñezó, R., “A fast converging optimal technique applied to path planning of hyper-redundant manipulators,” Mech. Mach. Theory 118, 231–246 (2017).CrossRefGoogle Scholar
Wang, H., Chen, J., Lau, H. Y. K. and Ren, H., “Motion planning based on learning from demonstration for multiple-segment exible soft robots actuated by electroactive polymers,” IEEE Robot. Autom. Lett. 1(1), 391–398 (2016).CrossRefGoogle Scholar
Tan, N., Yu, P., Zhong, Z. and Zhang, Y., “Data-driven control for continuum robots based on discrete zeroing neural networks,” IEEE Trans. Ind. Inform. 19(5), 7088–7098 (2023).CrossRefGoogle Scholar
Mu, Z., Chen, Y., Li, Z., C.Wang, N. D. and Qian, H., “A combined planning method based on biarc curve and bézier curve for concentric cable-driven manipulators working in confined environments,” IEEE/ASME Trans. Mechatron. 27(6), 4475–4486 (2022).CrossRefGoogle Scholar
Ataka, A., Qi, P., Shiva, A., Shafti, A., Wurdemann, H., Liu, H. and Althoefer, K.. “Real-time Pose Estimation and Obstacle Avoidance for Multi-segment Continuum Manipulator in Dynamic Environments,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, Daejeon, Korea (South) (2012) pp. 2012–2832.Google Scholar
Xu, D., Li, E., Guo, R., Liu, J. and Liang, Z., “A cable-driven hyperredundant manipulator: Obstacle-avoidance path planning and tension optimization,” IEEE Robot. Autom. Mag. 29(3), 107–126 (2022).CrossRefGoogle Scholar
Tian, Y., Zhu, X., Meng, D., Wang, X. and Liang, B., “An overall configuration planning method of continuum hyper-redundant manipulators based on improved artificial potential field method,” IEEE Robot. Autom. Lett. 6(3), 4867–4874 (2021).CrossRefGoogle Scholar
Mohammad, A., Russo, M., Fang, Y., Dong, X., Axinte, D. and Kell, J., “An efficient follow-the-leader strategy for continuum robot navigation and coiling,” IEEE Robot. Autom. Lett. 6(4), 7493–7500 (2021).CrossRefGoogle Scholar
Amanov, E., Nguyen, T.-D. and Burgner-Kahrs, J., “Tendon-driven continuum robots with extensible sections–A model-based evaluation of path-following motions,” Int. J. Robot. Res. 240(1), 7–23 (2021).CrossRefGoogle Scholar
Liu, T., Xu, W., Yang, T. and Li, Y., “A hybrid active and passive cable-driven segmented redundant manipulator: Design, kinematics, and planning,” IEEE/ASME Trans. Mechatron. 26(2), 930–942 (2021).CrossRefGoogle Scholar
Parvaresh, A. and Moosavian, S., “Dynamics and path tracking of continuum robotic arms using data-driven identification tools,” Robotica 40(4), 1098–1124 (2022).CrossRefGoogle Scholar
Niu, P., Han, L., Huang, Y. and Yan, L., “Shape-controllable inverse kinematics of hyper-redundant robots based on the improved FABRIK method,” Robotica 42(1), 225–241 (2024). doi: 10.1017/S0263574723001455.CrossRefGoogle Scholar
Pan, Z., Li, D., Yang, K. and Deng, H., “Multi-robot obstacle avoidance based on the improved artificial potential field and PID adaptive tracking control algorithm,” Robotica 37(11), 1883–1903 (2019).CrossRefGoogle Scholar
He, Z., Chu, X., Liu, C. and Wu, W., “A novel model predictive artificial potential field based ship motion planning method considering colregs for complex encounter scenarios,” ISA Trans. 134, 58–73 (2023).CrossRefGoogle ScholarPubMed
Jiang, X. and Li, S., “BAS: Beetle antennae search algorithm for optimization problems,” Int. J. Robot. Control 1(1), 1–5 (2018).CrossRefGoogle Scholar
Zhang, Y., Li, S. and Xu, B., “Convergence analysis of beetle antennae search algorithm and its applications,” Soft Comput. 25(16), 10595–10608 (2021).CrossRefGoogle Scholar
Wu, Q., Lin, H., Jin, Y., Chen, Z. and Chen, D., “A new fallback beetle antennae search algorithm for path planning of mobile robots with collision-free capability,” Soft Comput. 24(15), 2369–2380 (2020).CrossRefGoogle Scholar
Khan, A. H., Li, S. and Luo, X., “Obstacle avoidance and tracking control of redundant robotic manipulator: An RNN-based metaheuristic approach,” IEEE Trans. Ind. Inform. 16(7), 4670–4680 (2020).CrossRefGoogle Scholar
Khan, A. T., Li, S. and Zhou, X., “Trajectory optimization of 5-link biped robot using beetle antennae search,” IEEE Trans. Circuits Syst. II Express Briefs 68(10), 3276–3280 (2021).Google Scholar
Chen, D., Li, X. and Li, S., “A novel convolutional neural network model based on beetle antennae search optimization algorithm for computerized tomography diagnosis,” IEEE Trans. Neural Netw. Learn. Syst. 34(3), 1418–1429 (2023).CrossRefGoogle ScholarPubMed
Li, X., Jiang, H., Niu, M. and Wang, R., “An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm,” Mech. Syst. Signal Process 142, 106752 (2020).CrossRefGoogle Scholar
Huang, Q., Xie, L., Yin, G., Ran, M., Liu, X. and Zheng, J., “Acoustic signal analysis for detecting defects inside an arc magnet using a combination of variational mode decomposition and beetle antennae search,” ISA Trans. 102, 347–364 (2020).CrossRefGoogle ScholarPubMed
Zheng, X., Ding, M., Liu, L., Zhu, Y. and Guo, Y., “Static-to-kinematic modeling and experimental validation of tendon-driven quasi continuum manipulators with nonconstant subsegment stiffness,” Chin. Phys. B 33(1), 010703 (2024). doi: 10.1088/1674-1056/acfaf8.CrossRefGoogle Scholar
Webster, R. 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
Menasri, R., Nakib, A., Daachi, B., Oulhadj, H. and Siarry, P., “A trajectory planning of redundant manipulators based on bilevel optimization,” Appl. Math. Comput. 250, 934–947 (2015).Google Scholar
Tomasz, R., Mateusz, W. and Fatina, L. B., “Optimal collision-free path planning of a free-oating space robot using spline-based trajectories,” Acta Astronaut. 190, 395–408 (2022).Google Scholar