A hierarchical neuro-fuzzy system to near optimal-time trajectory planning of redundant manipulators

Authors:
Amar Khoukhi;Luc Baron;Marek Balazinski;Kudret Demirli
Affiliations:
Department of Mechanical Engineering, ícole Polytechnique de Montréal, C.P. 6079, Succ. CV, Montreal, Que., Canada H3C 3A7;Department of Mechanical Engineering, ícole Polytechnique de Montréal, C.P. 6079, Succ. CV, Montreal, Que., Canada H3C 3A7;Department of Mechanical Engineering, ícole Polytechnique de Montréal, C.P. 6079, Succ. CV, Montreal, Que., Canada H3C 3A7;Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve Blvd. W., EV4.173, Montreal, Que., Canada H3G 1M8
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

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Abstract

In this paper, the problem of minimum-time trajectory planning is studied for a three degrees-of-freedom planar manipulator using a hierarchical hybrid neuro-fuzzy system. A first neuro-fuzzy network named NeFIK is considered to solve the inverse kinematics problem. After a few pre-processing steps characterizing the minimum-time trajectory and the corresponding torques, a second neuro-fuzzy controller is built. Its purpose is to fit the robot dynamic behavior corresponding to the determined minimum-time trajectory with respect to actuators models, torque nominal values, as well as position, velocity, acceleration and jerk boundary conditions. A Tsukamoto Neuro-Fuzzy Inference network is designed to achieve the online control of the robot. The premise parameters (antecedent membership functions parameters) as well as rule-consequence parameters are learned and optimized, generating the optimal-time trajectory torques, representing the robot dynamic behavior. Simulation results are presented and discussed.