Abstract
With the wide deployment of kinematically redundant manipulators in complex working environments, obstacle avoidance emerges as an important issue to be addressed in robot motion planning. In this chapter, the inverse kinematic control of redundant manipulators for obstacle avoidance task is formulated as a convex quadratic programming (QP) problem subject to equality and inequality constraints with time-varying parameters. Compared with our previous formulation, the new scheme is more favorable in the sense that it can yield better solutions for the control problem. To solve this time-varying QP problem in real time, a recently proposed recurrent neural network, called an improved dual neural network, is adopted, which has lower structural complexity compared with existing neural networks for solving this particular problem. Moreover, different from previous work in this line where the nearest points to the links on obstacles are often assumed to be known or given, we consider the case of obstacles with convex hull and formulate another time-varying QP problem to compute the critical points on the manipulator. Since this problem is not strictly convex, an existing recurrent neural network, called a general projection neural network, is applied for solving it. The effectiveness of the proposed approaches is demonstrated by simulation results based on the Mitsubishi PA10-7C manipulator.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Al-Gallaf, E.A.: Multi-fingered robot hand optimal task force distribution - neural inverse kinematics approach. Robot. Auton. Syst. 54(1), 34–51 (2006).
Allotta, B., Colla, V., Bioli, G.: Kinematic control of robots with joint constraints. J. Dyn. Syst. Meas. Control-Trans. ASME 121(3), 433–442 (1999).
Cheng, F.T., Chen, T.H., Wang, Y.S., Sun, Y.Y.: Obstacle avoidance for redundant manipulators using the compact QP method. In: Proc. IEEE Int. Conf. Robotics and Automation (ICRA), vol. 3, pp. 262–269. Atlanta, Georgia, USA (1993).
Cheng, F.T., Lu, Y.T., Sun, Y.Y.: Window-shaped obstacle avoidance for a redundant manipulator. IEEE Trans. Syst., Man, Cybern. B 28(6), 806–815 (1998).
Ding, H., Chan, S.P.: A real-time planning algorithm for obstacle avoidance of redundant robots. Journal of Intelligent and Robotic Systems 16(3), 229–243 (1996).
Ding, H., Tso, S.K.: Redundancy resolution of robotic manipulators with neural computation. IEEE Trans. Industrial Electronics 46(1), 230–233 (1999).
Ding, H., Wang, J.: Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators. IEEE Trans. Syst., Man, Cybern. A 29(3), 269–276 (1999).
Forti, M., Nistri, P., Quincampoix, M.: Generalized neural network for nonsmooth nonlinear programming problems. IEEE Trans. Circuits Syst. I 51(9), 1741–1754 (2004).
Gao, X.B.: A neural network for a class of extended linear variational inequalities. Chinese Jounral of Electronics 10(4), 471–475 (2001).
Glass, K., Colbaugh, R., Lim, D., Seraji, H.: Real-time collision avoidance for redundant manipulators. IEEE Trans. Robot. Autom. 11(3), 448–457 (1995).
Guo, J., Hsia, T.C.: Joint trajectory generation for redundant robots in an environment with obstacles. Journal of Robotic Systems 10(2), 199–215 (1993).
Hopfield, J.J., Tank, D.W.: Computing with neural circuits: a model. Scienc 233(4764), 625– 633 (1986).
Hu, X., Wang, J.: Design of general projection neural networks for solving monotone linear variational inequalities and linear and quadratic optimization problems. IEEE Trans. Syst., Man, Cybern. B 37(5), 1414–1421 (2007).
Hu, X., Wang, J.: Solving generally constrained generalized linear variational inequalities using the general projection neural networks. IEEE Trans. Neural Netw. 18(6), 1697–1708 (2007).
Hu, X., Wang, J.: An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application. IEEE Trans. Neural Netw. (2008). Accepted
Liu, S., Hu, X., Wang, J.: Obstacle avoidance for kinematically redundant manipulators based on an improved problem formulation and the simplified dual neural network. In: Proc. IEEE Three-Rivers Workshop on Soft Computing in Industrial Applications, pp. 67–72. Passau, Bavaria, Germany (2007).
Liu, S., Wang, J.: A simplified dual neural network for quadratic programming with its KWTA application. IEEE Trans. Neural Netw. 17(6), 1500–1510 (2006).
Maciejewski, A.A., Klein, C.A.: Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. Intl. J. Robotics Research 4(3), 109–117 (1985).
Mao, Z., Hsia, T.C.: Obstacle avoidance inverse kinematics solution of redundant robots by neural networks. Robotica 15, 3–10 (1997).
Nakamura, Y., Hanafusa, H., Yoshikawa, T.: Task-priority based redundancy control of robot manipulators. Intl. J. Robotics Research 6(2), 3–15 (1987).
Ohya, I., Kosaka, A., Ka, A.: Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing. IEEE Trans. Robot. Autom. 14(6), 969–978 (1998).
Sciavicco, L., Siciliano, B.: Modeling and Control of Robot Manipulators. Springer-Verlag, London, U.K. (2000).
Shoval, S., Borenstein, J.: Using coded signals to benefit from ultrasonic sensor crosstalk in mobile robot obstacle avoidance. In: Proc. IEEE Int. Conf. Robotics and Automation (ICRA), vol. 3, pp. 2879–2884. Seoul, Korea (2001).
Tang, W.S., Lam, M., Wang, J.: Kinematic control and obstacle avoidance for redundant manipulators using a recurrent neural network. In: Proc. Intl. Conf. on Artificial Neural Networks, Lecture Notes in Computer Science, vol. 2130, pp. 922–929. Vienna, Austria (2001).
Tank, D.W., Hopfield, J.J.: Simple neural optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans. Circuits Syst. 33(5), 533–541 (1986).
Walker, I.D., Marcus, S.I.: Subtask performance by redundancy resolution for redundant robot manipulators. IEEE J. Robot. Autom. 4(3), 350–354 (1988).
Wang, J., Hu, Q., Jiang, D.: A lagrangian network for kinematic control of redundant robot manipulators. IEEE Trans. Neural Netw. 10(5), 1123–1132 (1999).
Xia, Y., Feng, G., Wang, J.: A primal-dual neural network for on-line resolving constrained kinematic redundancy in robot motion control. IEEE Trans. Syst., Man, Cybern. B 35(1), 54–64 (2005).
Xia, Y., Wang, J.: A general methodology for designing globally convergent optimization neural networks. IEEE Trans. Neural Netw. 9(6), 1331–1343 (1998).
Xia, Y., Wang, J.: A general projection neural network for solving monotone variational inequalities and related optimization problems. IEEE Trans. Neural Netw. 15(2), 318–328 (2004).
Xia, Y., Wang, J., Fok, L.M.: Grasping force optimization of multi-fingered robotic hands using a recurrent neural network. IEEE Transactions on Robotics and Automation 20(3), 549– 554 (2004).
Zhang, Y., Ge, S.S., Lee, T.H.: A unified quadratic programming based dynamical system approach to joint torque optimization of physically constrained redundant manipulators. IEEE Trans. Syst., Man, Cybern. B 34(5), 2126–2132 (2004).
Zhang, Y., Wang, J.: Obstacle avoidance for kinematically redundant manipulators using a dual neural network. IEEE Trans. Syst., Man, Cybern. B 34(1), 752–759 (2004).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer London
About this chapter
Cite this chapter
Wang, J., Hu, X., Zhang, B. (2009). Real-time Motion Planning of Kinematically Redundant Manipulators Using Recurrent Neural Networks. In: Yu, W. (eds) Recent Advances in Intelligent Control Systems. Springer, London. https://doi.org/10.1007/978-1-84882-548-2_8
Download citation
DOI: https://doi.org/10.1007/978-1-84882-548-2_8
Publisher Name: Springer, London
Print ISBN: 978-1-84882-547-5
Online ISBN: 978-1-84882-548-2
eBook Packages: EngineeringEngineering (R0)