Affiliations: [a] Center for Advanced Manufacturing, University of Southern California, CA, USA | [b] Southwest Research Institute, TX, USA | [c] Department of Mechanical and Aerospace Engineering, Old Dominion University, VA, USA
Correspondence:
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Corresponding author: Satyandra K. Gupta, Center for Advanced Manufacturing, University of Southern California, CA, USA. E-mail: [email protected].
Abstract: In this paper, we have presented a method to identify optimal trajectory parameters in process applications like robotic finishing, while minimizing the required number of physical experiments. Our method optimizes the task objective and meets the task performance constraints. The algorithm makes decisions based on the uncertainty in the surrogate models of task performances or black-box constraints. The method intelligently samples the parameter space to select a point for evaluation from the sampled set by determining its probability to be optimum within the set. The iterative method rapidly converges to the optimal point with a small number of experiments. We have proved that our method will converge faster than any other method in the expected sense. We have considered synthetic test problems to benchmark our method against other methods. We have validated our approach through physical experiments of robotic scrubbing and sanding. This algorithm is general enough to be applicable to mathematical constraint satisfaction problems where the objective function is known and the constraint functions are unknown.
