Trajectory planning of a robot using learning algorithms
Tsoularis, A., Kambhampati, C. and Warwick, K. (1992) Trajectory planning of a robot using learning algorithms. In: First International Conference on Intelligent Systems Engineering, 1992. IEE, pp. 13-16. ISBN 0852965494
Full text not archived in this repository.
The authors consider the problem of a robot manipulator operating in a noisy workspace. The manipulator is required to move from an initial position P(i) to a final position P(f). P(i) is assumed to be completely defined. However, P(f) is obtained by a sensing operation and is assumed to be fixed but unknown. The authors approach to this problem involves the use of three learning algorithms, the discretized linear reward-penalty (DLR-P) automaton, the linear reward-penalty (LR-P) automaton and a nonlinear reinforcement scheme. An automaton is placed at each joint of the robot and by acting as a decision maker, plans the trajectory based on noisy measurements of P(f).
Centaur Editors: Update this record