UC San Diego

This dissertation considers the problem of safe navigation for autonomous mobile robots working in partially known and unknown environments with static and non-adversarial moving obstacles. Given a geometric path generated by standard path planner, we develop reference-governor based tracking control policy to continuous generate proper set-points along the path for downstream low-level stabilizing controller.

This new method systematically puts planning, motion prediction and safety metric design together to achieve environmentally adaptive and safe navigation. Our algorithm balances optimality in travel distance and safety regarding passing clearance. Robots adapt progress speed adaptively according to the sensed environment, being fast in wide open areas and slowdown in narrow passages and taking necessary maneuvers to avoid dangerous incoming obstacles. Directional distance measure, motion prediction and custom costmap are integrated properly to evaluate system risk accurately with respect to local geometry of surrounding environments. Using such risk estimation, reference governor technique and control barrier function are worked together to enable adaptive and safe path tracking in dynamical environments. We validate our algorithm extensively both in simulations and hardware platforms in challenging real-world environments.