Acceleration of the Multilayer Network Operator Method Using MPI for Mobile Robot Team Control Synthesis

The paper is devoted to the problem of machine-made synthesis of control for robotic teams. The goal of synthesis is to find a multidimensional control function that depends on the current states of all robots. The synthesised control function provides any time the optimal control values to allow each robot achieving the objectives with the best value of functional quality. The approach is based on multilayer network operator method that belongs to a symbolic regression class. Formations of multi-robot systems require individual robots to satisfy their kinematic equations while constantly maintaining inter-robot dynamic constraints. Verification of these dynamic constraints on each iteration of the evolutionary algorithm greatly increases the computational costs of the numerical synthesis. In the paper we propose to accelerate existing designs through taking advantage of newest programming tools of MPI framework for automatic parallelization. Experiments show that our approach reduces greatly computational time.