Trajectory planning of a robot using learning algorithms

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/sub i/ to a final position P/sub f/. P/sub i/ is assumed to be completely defined. However, P/sub 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 (DL/sub R-P/) automaton, the linear reward-penalty (L/sub R-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/sub f/.<
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