A sampling-based strategy planner for nondeterministic hybrid systems

This paper introduces a strategy planner for nondeterministic hybrid systems with complex continuous dynamics. The planner uses sampling-based techniques and game-theoretic approaches to generate a series of plans and decision choices that increase the chances of success within a fixed time budget. The planning algorithm consists of two phases: exploration and strategy improvement. During the exploration phase, a search tree is grown in the hybrid state space by sampling state and control spaces for a fixed amount of time. An initial strategy is then computed over the search tree using a game-theoretic approach. To mitigate the effects of nondeterminism in the initial strategy, the strategy improvement phase extends new tree branches to the goal, using the data that is collected in the first phase. The efficacy of this planner is demonstrated on simulation of two hybrid and nondeterministic car-like robots in various environments. The results show significant increases in the likelihood of success for the strategies computed by the two-phase algorithm over a simple exploration planner.