Optimal Navigation for Vehicles With Stochastic Dynamics

Abstract

This brief presents a framework for input-optimal navigation under state constraints for vehicles exhibiting stochastic behavior. The resulting stochastic control law is implementable in real time on vehicles with limited computational power. When control actuation is unconstrained, then convergence with probability 1 can be theoretically guaranteed. When inputs are bounded, the probability of convergence is quantifiable. The experimental implementation on a 5.5 g, 720-MHz processor that controls a bioinspired crawling robot with stochastic dynamics, corroborates the design framework.