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This work presents an optimal sampling-based method to solve the real-timemotion planning problem in static and dynamic environments, exploiting theRapid-exploring Random Trees (RRT) algorithm and the Model Predictive PathIntegral (MPPI) algorithm. The RRT algorithm provides a nominal mean value ofthe random control distribution in the MPPI algorithm, resulting insatisfactory control performance in static and dynamic environments without aneed for fine parameter tuning. We also discuss the importance of choosing theright mean of the MPPI algorithm, which balances exploration and optimalitygap, given a fixed sample size. In particular, a sufficiently large mean isrequired to explore the state space enough, and a sufficiently small mean isrequired to guarantee that the samples reconstruct the optimal controls. Theproposed methodology automates the procedure of choosing the right mean byincorporating the RRT algorithm. The simulations demonstrate that the proposedalgorithm can solve the motion planning problem in real-time for static ordynamic environments.

RRT Guided Model Predictive Path Integral Method