Parallel Optimization of Motion Controllers via Policy Iteration

This paper describes a policy iteration algorithm for optimizing the performance of a harmonic function-based controller with respect to a user-defined index. Value functions are represented as potential distributionsover the problem domain, being control policies represented as gradient fields over the same domain. All intermediate policiesare intrinsically safe, i.e. collisions are not promoted during the adaptation process. The algorithm has efficient implementation inparallel SIMD architectures. One potential application - travel distance minimization - illustrates its usefulness.