Parametric optimization of unmanned vehicle controller by PSO algorithm

Abstract

For an unmanned vehicle, in difficult conditions, when spatial constraints seriously narrow the space of admissible states, the strategy of choosing a state space is more effective than sampling in the control space. Although this was obvious, the practical question is how to achieve it while meeting the stringent constraints of the vehicle’s dynamic feasibility.This article presents an unmanned vehicle control system based on the predictive integrated path model (MPPI) controller, deep convolutional neural network (CNN) for real-time scene understanding and particle swarm optimization (PSO) to find the vector of optimal cost function parameters. The method is based on the optimization of the cost function, which determines where the vehicle should move on the surface of the path.