Time-Optimal Path Planning and Control Using Neural Networks and a Genetic Algorithm

This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control to minimize command tracking errors. The extended Kohonen networks are trained using reinforcement learning where the overall learning algorithm is derived from a self-organizing feature-mapping algorithm and a delta learning rule. The results indicate that the extended Kohonen network controller is more efficient than other techniques reported in early literature when the robot is operated under normal conditions. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimization problem related to time-optimal control. This problem involves the selection of actuator torque limits and an end-effector path subject to time-optimality and tracking error constraints. Two chromosome coding schemes are explored in the investigation: Gray and integer-based coding schemes. The results suggest that the integer-based chromosome is more suitable at representing the decision variables. As a result of using both neural networks and a genetic algorithm in this application, an idea of a hybridization between a neural network and a genetic algorithm at the task level for use in a control system is also effectively demonstrated.

[1]  Zvi Shiller,et al.  The practical implementation of time-optimal control for robotic manipulators , 1996 .

[2]  J. Bobrow,et al.  Time-Optimal Control of Robotic Manipulators Along Specified Paths , 1985 .

[3]  Z. Shiller,et al.  Computation of Path Constrained Time Optimal Motions With Dynamic Singularities , 1992 .

[4]  Y. H. Chen.  Decentralized Robust Control for Large-Scale Uncertain Systems: A Design Based on the Bound of Uncertainty , 1992 .

[5]  Zvi Shiller,et al.  Time-Energy Optimal Control of Articulated Systems With Geometric Path Constraints , 1996 .

[6]  Peter J. Fleming,et al.  Multiobjective genetic algorithms made easy: selection sharing and mating restriction , 1995 .

[7]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[8]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[9]  E. Freund.  Fast Nonlinear Control with Arbitrary Pole-Placement for Industrial Robots and Manipulators , 1982 .

[10]  Andrzej Osyczka,et al.  Multicriteria Design Optimization: Procedures and Applications , 1990 .

[11]  Helge J. Ritter,et al.  Three-dimensional neural net for learning visuomotor coordination of a robot arm , 1990, IEEE Trans. Neural Networks.

[12]  L. G. van Willigenburg,et al.  Time-optimal path planning and control of robot manipulators for fruit harvesting , 2000 .

[13]  Ali M. S. Zalzala,et al.  Hybridisation of neural networks and genetic algorithms for time-optimal control , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[14]  M. Kawato,et al.  Hierarchical neural network model for voluntary movement with application to robotics , 1988, IEEE Control Systems Magazine.

[15]  J. E. Baker,et al.  An analysis of the effects of selection in genetic algorithms , 1989 .

[16]  John M. Hollerbach,et al.  Planning of Minimum- Time Trajectories for Robot Arms , 1986 .