Combined stochastic-deterministic predictive control using local-minima free navigation

This work proposes a model predictive control approach of a wheeled mobile robot based on a local-minima free navigation function. The constructed navigation function includes information on a goal location and obstacles. Novel conservative navigation is introduced that is simple to compute and yields convergent control behavior. To solve the optimization problem the combined optimization is proposed by a fixed candidate set and particle swarm optimization. The efficiency of the proposed approaches is validated on simulations, by experimental results on the Husky A200 mobile robot and comparisons to Stable Sparse RRT planner.