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Fuzzy Hint Acquisition for the Collision Avoidance Solution of Redundant Manipulators Using Neural Network

Samy F. M. Assal, Keigo Watanabe, and Kiyotaka Izumi
International Journal of Control, Automation, and Systems, vol. 4, no. 1, pp.17-29, 2006

Abstract : A novel inverse kinematics solution based on the back propagation neural network (NN) for redundant manipulators is developed for online obstacles avoidance. A laser transducer at the end-effctor is used for online planning the trajectory. Since the inverse kinematics in the present problem has infinite number of joint angle vectors, a fuzzy reasoning system is designed to generate an approximate value for that vector. This vector is fed into the NN as a hint input vector rather than as a training vector to guide the output of the NN. Simulations are implemented on both three- and four-link redundant planar manipulators to show the effectiveness of the proposed position control system.

Keyword : Fuzzy system, inverse kinematics, neural networks, online collision avoidance, redundant manipulators.

 
 
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