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- W4382935863 abstract "This work presents an optimal sampling-based method to solve the real-time motion planning problem in static and dynamic environments, exploiting the Rapid-exploring Random Trees (RRT) algorithm and the Model Predictive Path Integral (MPPI) algorithm. The RRT algorithm provides a nominal mean value of the random control distribution in the MPPI algorithm, resulting in satisfactory control performance in static and dynamic environments without a need for fine parameter tuning. We also discuss the importance of choosing the right mean of the MPPI algorithm, which balances exploration and optimality gap, given a fixed sample size. In particular, a sufficiently large mean is required to explore the state space enough, and a sufficiently small mean is required to guarantee that the samples reconstruct the optimal control. The proposed methodology automates the procedure of choosing the right mean by incorporating the RRT algorithm. The simulations demonstrate that the proposed algorithm can solve the motion planning problem for static or dynamic environments." @default.
- W4382935863 created "2023-07-04" @default.
- W4382935863 creator A5012273541 @default.
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- W4382935863 date "2023-05-31" @default.
- W4382935863 modified "2023-09-25" @default.
- W4382935863 title "RRT Guided Model Predictive Path Integral Method" @default.
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- W4382935863 doi "https://doi.org/10.23919/acc55779.2023.10155837" @default.
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