A Biologically Inspired Neural Netfor Trajectory Formation

for Trajectory Formation and Obstacle Avoidance. 1 R. Glasius A. Komoda S. Gielen Department of Medical Physics and Biophysics, University of Nijmegen, Geert Grooteplein Noord 21, 6525 EZ Nijmegen, The Netherlands, Abstract A biologically inspired two-layered neural network for trajectory formation and obstacle avoidance is presented. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The rst layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solutions (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time-evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of the actuators of a biological limb or robot manipulator. Even if an external force acts on the limb or manipulator the system is able to reach one of the targets. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.