Abstract
This paper deals with minimum time trajectory optimization along a specified path subject to thermal constraints. We point out here that robots are often integrated into complex robotic cells, and the interactions between the robot and its environment are often difficult or even impossible to model. The structure of the optimization problem allows us to decompose the optimization into two levels, the first being based on models and results of the theory of the calculus of variations, the second being based on measurements and derivative free algorithms. This decomposition allows us to optimize the velocity profiles efficiently without any advance knowledge of the interactions between the robot and its environment. We propose here two numerical algorithms for these two levels of the decomposition which show good convergence properties. The resulting optimal velocity profiles are 5—10% faster than classical profiles, and have been executed successfully on a real Stäubli Rx90 manipulator robot.
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