Abstract
This article presents an intelligent control system for a redundant manipulator to avoid physical limits such as joint angle limits and joint velocity limits. In this method, a back-propagation neural network (NN) is introduced for the kinematic inversion of the manipulator. Since this inverse kinematics has an infinite number of joint angle vectors, a fuzzy-neuro system is constructed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector in order to guide the output of the NN within the self-motion. Simulations and a comparative study are made based on a four-link redundant manipulator to prove the efficacy of the proposed control system.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
M Liegeois (1977) ArticleTitleAutomatic supervisory control of the configuration and behavior of multibody mechanisms IEEE Trans Syst Man Cybern SMC-7 IssueID12 868–871
Zghal H, Dubey RV, Euler JA (1990) Efficient gradient projection optimization for manipulators with multiple degrees of redundancy. Proceedings of the IEEE International Conference on Robotics and Automation, vol 2, pp 1006–1011
TF Chan RV Dubey (1995) ArticleTitleA weighted least-norm solution-based scheme for avoiding joint limits for redundant joint manipulators IEEE Trans Robot Autom 11 286–292 Occurrence Handle10.1109/70.370511
F-T Cheng T-H Chen Y-Y Sun (1994) ArticleTitleResolving manipulator redundancy under inequality constraints IEEE Trans Robot Autom 10 65–71 Occurrence Handle10.1109/70.285587
F-T Cheng RJ Sheu T-H Chen (1995) ArticleTitleThe improved compact QP method for resolving manipulator redundancy IEEE Trans Syst Man Cybern 25 1521–1530 Occurrence Handle10.1109/21.467718
Ahson SI, Sharkey NE, Nicolas B (1996) Avoiding joint limits and obstacles for kinematically redundant manipulators: a fuzzy-logic-based approach. Proceedings of the 5th IEEE International Conference on Fuzzy Systems, vol 3, pp 1777–1781
MC Ramos AJ Koivo (2002) ArticleTitleFuzzy-logic-based optimization for redundant manipulators IEEE Trans Fuzzy Syst 10 498–509 Occurrence Handle10.1109/TFUZZ.2002.800684
Daachi B, Madani T, Ramdane-Cherif A, et al. (2003) Memory neural network for kinematic inversion of constrained redundant robot manipulators. Proceedings of the 11th International Conference on Advanced Robotics, pp 1855–1862
Assal SFM, Watanabe K, Izumi K (2004) Cooperative fuzzy hint acquisition for avoiding joint limits of redundant manipulators. Proceedings of the 30th Annual Conference of the IEEE Industrial Electronic Society (IECON 2004), November, WC5-6 in CD
Y Zhang J Wang Y Xia (2003) ArticleTitleA dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and joint velocity limits IEEE Trans Neural Networks 14 658–667 Occurrence Handle10.1109/TNN.2003.810607
Riedmiller M, Braun H (1993) A direct adaptive method for faster back-propagation learning: the RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks, pp 586–591
S Haykin (1994) Neural networks: a comprehensive foundation Macmillan New York
Assal SFM, Watanabe K, Izumi K (2004) Exploring motion acquisition of manipulators with multiple degrees of redundancy using soft computing techniques. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), pp 3086–3091
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Assal, S., Watanabe, K. & Izumi, K. Intelligent control for avoiding the joint limits of redundant planar manipulators. Artif Life Robotics 10, 141–148 (2006). https://doi.org/10.1007/s10015-005-0370-8
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s10015-005-0370-8