A Neural Network Based Collision Detection Engine for Multi-Arm Robotic Systems

Rana, A.S.; Zalzala, A.M.S. (1996)
Department of Automatic Control and Systems Engineering
A neural ntwork is proposed for collision detection among multiple robotic arms sharing a common workspace. The structure of the neural network is a hybrid between Guassian Radial Basis Function (RBF) neural networks and Multi-layer perceptron back-propagation (BP) neural networks. This network is used to generate potential fields in the configuration space of the robotic arms. A path planning algorithm based on heuristics is presented. It is shown that this algorithm works better than the conventional potential field methods which carry out the planning in the operational space of robots. To show the effectiveness of the algorithm, simulation results are presented for a single 2-DOF robotic arm in presence of a static obstacle and then for two plannar manipulator sharing a common workspace. The algorithm is then extended to the case of 3-DOF arms moving in 3-D space.

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