Computational Intelligence Based Approach for the Joint Trajectory Generation of Cooperative Robotic Systems

Abstract

We discuss here the implementation aspects of recently developed tools of computational intelligence as applied to joint trajectory generation of a class of multi-joint cooperative robotic systems. This is an issue closely related to the inverse kinematics problem which usually represents a heavy computational burden on the processing power of any complex robotic structure. High nonlinearities, heavy coupling between the degrees of freedom, and time variant configuration of the robot structure heavily contribute to these difficulties. Soft computing techniques have surged in recent years as effective computational tools for emulating the human capabilities when dealing with complex systems. Some of them are used here to synthesize approaches capable of substantially improving the solving of the inverse kinematics problem for a class of robotic systems and help in generating the joint trajectories in a faster way. Comparative results are provided in terms of accuracy and CPU time required for the execution of different trajectories.