Hostname: page-component-6766d58669-nqrmd Total loading time: 0 Render date: 2026-05-20T23:38:05.241Z Has data issue: false hasContentIssue false
  • English
  • Français

An obstacle avoidance algorithm for space hyper-redundant manipulators using combination of RRT and shape control method

Published online by Cambridge University Press:  03 August 2021

Xiaobo Zhang Open the ORCID record for Xiaobo Zhang [Opens in a new window] ,
Jinguo Liu  and
Yangmin Li Open the ORCID record for Yangmin Li [Opens in a new window]
Xiaobo Zhang
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China University of Chinese Academy of Sciences, Beijing 100049, China
Jinguo Liu*
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
Yangmin Li
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
*
*Corresponding author. E-mail: liujinguo@sia.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper proposes a kinematic obstacle avoidance algorithm for Space hyper-redundant manipulators, and its basic idea is to use a static and a dynamic curve to constrain the macroshape of the manipulators simultaneously. The static curve is constructed based on a traditional rapidly exploring random tree algorithm, and a backbone curve is utilized as the dynamic curve. For these two curves, two novel shape control methods are proposed to accomplish the shape constraining process. Finally, we verify the reliability and effectiveness of our algorithm through simulations.

Keywords

obstacle avoidanceRRTshape controlspace hyper-redundant manipulators

Information

Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 1036 - 1069
Copyright
© The Author(s), 2021. 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

Chirikjian, G. S. and Burdick, J. W., “A modal approach to hyper-redundant manipulator kinematics,” IEEE Trans. Rob. Autom. 10(3), 343–354 (1994).CrossRefGoogle Scholar
Chirikjian, G. S. and Burdick, J. W., “Hyper-redundant Robot Mechanisms and Their Applications,” Proceedings of the IEEE RSJ International Workshop Intelligence Robots and Systems (IROS) (1991) pp. 185--190.Google Scholar
Wolf, A., Brown, H. B., Casciola, R., Costa, A., Schwerin, M., Shamas, E. and Choset, H., “A Mobile Hyper Redundant Mechanism for Search and Rescue Tasks,” Proceedings of IEEE International Conference on Intelligent Robots and Systems (2003) pp. 2889--2895.Google Scholar
Tang, J., Zhang, Y., Huang, F., Li, J., Chen, Z., Song, W., Zhu, S. and Gu, J., “Design and kinematic control of the cable-driven hyper-redundant manipulator for potential underwater applications,” Appl. Sci. 9(6), article number 1142 (2019).10.3390/app9061142CrossRefGoogle Scholar
Wan, W., Sun, C. and Yuan, J., “Adaptive caging configuration design algorithm of hyper-redundant manipulator for dysfunctional satellite pre-capture,” IEEE Access 8, 22546–22559 (2020).CrossRefGoogle Scholar
Zhang, X. and Liu, J., “Effective motion planning strategy for space robot capturing targets under consideration of the berth position,” Acta Astronaut. 148, 403--416 (2018).CrossRefGoogle Scholar
Zhang, X., Liu, J., Feng, J., Liu, Y. and Ju, Z., “Effective capture of nongraspable objects for space robots using geometric cage pairs,” IEEE/ASME Trans. Mechatron. 25(1), 95–107 (2020).CrossRefGoogle Scholar
Mu, Z., Liu, T., Xu, W., Lou, Y. and Liang, B., “Dynamic feedforward control of spatial cable-driven hyper-redundant manipulators for on-orbit servicing,” Robotica 37(1), 18–38 (2019).CrossRefGoogle Scholar
Rybus, T., “Obstacle avoidance in space robotics: Review of major challenges and proposed solutions,” Prog. Aeosp. Sci. 101, 31–48 (2018).CrossRefGoogle Scholar
Marcos, M. D., Machado, J. A. T. and Azevedo-Perdicoulis, T.-P., “A fractional approach for the motion planning of redundant and hyper-redundant manipulators,” Signal Process. 91(3), 562–570 (2011).10.1016/j.sigpro.2010.01.016CrossRefGoogle Scholar
Maciejewski, A. A. and Klein, C. A., “Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments,” Int. J. Robot. Res. 4(3), 109–117 (1985).CrossRefGoogle Scholar
Qiu, C., Cao, Q. and Miao, S., “An on-line task modification method for singularity avoidance of robot manipulators,” Robotica 27(4), 539–546 (2009).CrossRefGoogle Scholar
Liao, B. and Liu, W., “Pseudoinverse-type bi-criteria minimization scheme for redundancy resolution of robot manipulators,” Robotica 33(10), 2100–2113 (2015).CrossRefGoogle Scholar
Wan, J., Wu, H., Ma, R. and Zhang, L., “A study on avoiding joint limits for inverse kinematics of redundant manipulators using improved clamping weighted least-norm method,” J. Mech. Sci. Technol. 32(3), 1367–1378 (2018).10.1007/s12206-018-0240-7CrossRefGoogle Scholar
Ma, S. and Nenchev, D., “Local torque minimization for redundant manipulators: A correct formulation,” Robotica 14(2), 235–239 (1996).10.1017/S0263574700019159CrossRefGoogle Scholar
Barraquand, J., Langlois, B. and Latombe, J. C., “Numerical potential field techniques for robot path planning,” IEEE Trans. Syst. Man. Cybern. 22(2), 224--241 (1992).CrossRefGoogle Scholar
Barraquand, J. and Latombe, J. C., “Robot motion planning: A distributed representation approach,” Int. J. Robot. Res. 10(6), 628–649 (1991).CrossRefGoogle Scholar
Conkur, E. S., “Path planning using potential fields for highly redundant manipulators,” Robot. Auton. Syst. 52(2--3), 209–228 (2005).CrossRefGoogle Scholar
Lozano-Perez, T., “Spatial planning: A configuration space approach,” IEEE Trans. Comput. 32(2), 108–120 (1983).CrossRefGoogle Scholar
Kavraki, L. E., Svestka, P., Latombe, J. C. and Overmars, M. H., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Rob. Autom. 12(4), 566–580 (1996).10.1109/70.508439CrossRefGoogle Scholar
Dasgupta, B., Gupta, A. and Singla, E., “A variational approach to path planning for hyper-redundant manipulators,” Robot. Auton. Syst. 57(2), 194–201 (2009).CrossRefGoogle Scholar
Marcos, M. D., Machado, J. A. T. and Azevedo-Perdicoulis, T.-P., “Trajectory planning of redundant manipulators using genetic algorithms,” Commun. Nonlinear Sci. Numer. Simul. 14(7), 2858–2869 (2009).10.1016/j.cnsns.2008.10.014CrossRefGoogle Scholar
Zhao, J., Zhao, L. and Liu, H., “Motion Planning of Hyper-redundant Manipulators Based on Ant Colony Optimization,” Proceedings of IEEE International Conference on Robotics and Biomimetics, (ROBIO) (2016) pp. 1250--1255.CrossRefGoogle Scholar
Ananthanarayanan, H. and Ordez, R., “A fast converging optimal technique applied to path planning of hyper-redundant manipulators,” Mech. Mach. Theory 118, 231–246 (2017).10.1016/j.mechmachtheory.2017.08.005CrossRefGoogle Scholar
Kuffner, J. J. and Lavalle, S. M., “RRT-Connect: A Efficient Approach to Single-query Path Planning,” Proceedings of IEEE International Conference on Robotics and Automation (2000) pp. 995--1001.Google Scholar
Weghe, M. V., Ferguson, D. and Srinivasa, S. S., “Randomized Path Planning for Redundant Manipulators without Inverse Kinematics,” Proceedings of IEEE-RAS International Conference on Humanoid Robots (2007) pp. 477--482.Google Scholar
Vahrenkamp, N., Berenson, D., Asfour, T., Kuffner, J. and Dillmann, R., “Humanoid Motion Planning for Dual-Arm Manipulation and Re-Grasping Tasks,” Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS) (2009) pp. 2464--2470.Google Scholar
Shkolnik, A. and Tedrake, R., “Path Planning in 1000+ Dimensions using a Task-Space Voronoi Bias,” Proceedings of IEEE International Conference on Robotics and Automation (2009) pp. 2061--2067.Google Scholar
Mesesan, G., Roa, M. A., Icer, E. and Althoff, M., “Hierarchical Path Planner using Workspace Decomposition and Parallel Task-Space RRTs,” Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 1--9.Google Scholar
Maciejewski, A. A. and Fox, J. J., “Path planning and the topology of configuration space,” IEEE Trans. Rob. Autom. 9(4), 444–456 (1993).CrossRefGoogle Scholar
Wang, J., Meng, M. Q. and Khatib, O., “EB-RRT: Optimal motion planning for mobile robots,” IEEE Trans. Autom. Sci. Eng. 17(4), 2063–2073 (2020).10.1109/TASE.2020.2987397CrossRefGoogle Scholar
Zhang, Y. and Wang, J., “Obstacle avoidance for kinematically redundant manipulators using a dual neural network,” IEEE Trans. Syst. Man. Cybern. 34(1), 752--759 (2004).CrossRefGoogle Scholar
Guo, D. and Zhang, Y., “A new inequality-based obstacle-avoidance MVN scheme and its application to redundant robot manipulators,” IEEE Trans. Syst. Man. Cybern. 42(6), 1326--1340 (2012).CrossRefGoogle Scholar
Guo, D. and Zhang, Y., “Acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators,” IEEE Trans. Ind. Electron. 61(12), 6903–6914 (2014).CrossRefGoogle Scholar
Hassan, A., M. EI-Habrouk and S. Deghedie, “Inverse kinematics of redundant manipulators formulated as quadratic programming optimization problem solved using recurrent neural networks: A review,” Robotica 38(8), 1495–1512 (2020).CrossRefGoogle Scholar
Zhang, Z., Zheng, L., Yu, J., Li, Y. and Yu, Z., “Three recurrent neural networks and three numerical methods for solving a repetitive motion planning scheme of redundant robot manipulators,” IEEE/ASME Trans. Mech. 22(3), 1423–1434 (2017).CrossRefGoogle Scholar
Menon, M. S., Ravi, V. C. and Ghosal, A., “Trajectory planning and obstacle avoidance for hyper-redundant serial robots,” J. Mech. Robot. 9(4), 041010 (2017).CrossRefGoogle Scholar
Ashwin, K. P., Chaudhury, A. N. and Ghosal, A., “Efficient representation of ducts and cluttered spaces for realistic motion planning of hyper-redundant robots through confined paths,” Comput.-Aided Des. 119, article number 102777 (2020).10.1016/j.cad.2019.102777CrossRefGoogle Scholar
Chirikjian, G. S. and Burdick, J. W., “An Obstacle Avoidance Algorithm for Hyper-redundant Manipulators,” Proceedings of IEEE International Conference on Robotics and Automation (1990) pp. 625--631.CrossRefGoogle Scholar
Choset, H. and Henning, W., “A follow-the-leader approach to serpentine robot motion planning,” J. Aerosp. Eng. 12(2), 65--73 (1999).CrossRefGoogle Scholar
Fahimi, F., Ashrafiuon, H. and Nataraj, C., “Obstacle avoidance for spatial hyper-redundant manipulators using harmonic potential functions and the mode shape technique,” J. Rob. Syst. 20(1), 23–33 (2003).CrossRefGoogle Scholar
Mu, Z., Liu, T., Xu, W., Lou, Y. and Liang, B., “A hybrid obstacle-avoidance method of spatial hyper-redundant manipulators for servicing in confined space,” Robotica 37(6), 998–1019 (2019).CrossRefGoogle Scholar
Ma, S., Watanabe, M. and Kondo, H., “Dynamic Control of Curve-Constrained Hyper-redundant Manipulators,” Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) (2001) pp. 83--88.Google Scholar
Sreenivasan, S., Goel, P. and Ghosal, A., “A real-time algorithm for simulation of flexible objects and hyper-redundant manipulators,” Mech. Mach. Theory 45(3), 454–466 (2010).CrossRefGoogle Scholar
Menon, M. S., Ananthasuresh, G. K. and Ghosal, A., “Natural motion of one-dimensional flexible objects using minimization approaches,” Mech. Mach. Theory 67, 64–76 (2013).10.1016/j.mechmachtheory.2013.04.003CrossRefGoogle Scholar
Menon, M. S., Gurumoorthy, B. and Ghosal, A., “Efficient simulation and rendering of realistic motion of one-dimensional flexible objects,” Comput. -Aided Des. 75, 13–26 (2016).CrossRefGoogle Scholar
Zhang, X., Liu, J., Ju, Z. and Yang, C., “Head-raising of snake robots based on a predefined spiral curve method,” Appl. Sci. 8(11), 1–20 (2018).Google Scholar
Fahimi, F., Asharafiuon, H. and Nataraj, C., “An improved inverse kinematic and velocity solution for spatial hyper-redundant robots,” IEEE Trans. Rob. Autom. 18(1), 103--107 (2002).CrossRefGoogle Scholar
Xu, W., Mu, Z., Liu, T. and Liang, B., “A modified modal method for solving the mission-oriented inverse kinematics of hyper-redundant space manipulators for on-orbit servicing,” Acta Astronaut. 139, 54–66 (2017).CrossRefGoogle Scholar
Ananthanarayanan, H. and Ordez, R., “Real-time inverse kinematics of (2n+1) DOF hyper-redundant manipulator arm via a combined numerical and analytical approach,” Mech. Mach. Theory 91, 209–226 (2015).10.1016/j.mechmachtheory.2015.04.011CrossRefGoogle Scholar
Xidias, E. K., “Time-optimal trajectory planning for hyper-redundant manipulators in 3D workspaces,” Robot. Comput.-Integr. Manuf. 50, 286–298 (2018).CrossRefGoogle Scholar
Barr, A. H., “Superquadrics and angle-preserving transformations,” IEEE Comput. Graph. Appl. 1(1), 11–23 (1981).10.1109/MCG.1981.1673799CrossRefGoogle Scholar
Bradshaw, G. and O’Sullivan, C., “Adaptive medial-axis approximation for sphere-tree construction,” ACM Trans. Graph. 23(1), 1–26 (2004).CrossRefGoogle Scholar
Stolpner, S., Kry, P. and Siddiqi, K., “Medial spheres for shape approximation,” IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1234–1240 (2012).CrossRefGoogle ScholarPubMed
Supplementary material: File

Zhang et al. supplementary material

Zhang et al. supplementary material

Download Zhang et al. supplementary material(File)
File 56.5 MB