On using stochastic automata for trajectory planning of robot manipulators in noisy workspaces

The authors consider the problem of a robot manipulator operating in a noisy workspace. The robot is assigned the task of moving from an initial position P_i to a final position P _f. Since P_i this position can be known fairly accurately. However, since P_f is usually obtained as a result of a sensing operation, possibly vision sensing, the authors assume that P_f is noisy. The authors propose a solution to achieve the motion which involves a learning automaton, called the discretized linear reward-penalty (DL_RP) automaton. Alternatively, an automaton is positioned at each joint of the robot, and by processing repeated noisy observations of P_f the automata operate in parallel to control the motion of the manipulator. The advantages and the possible disadvantages of the scheme are also discussed