RRT Guided Model Predictive Path Integral Method
RRT Guided Model Predictive Path Integral Method
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DOI：
10.23919/acc55779.2023.10155837
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Chuyuan Tao;Hunmin Kim;N. Hovakimyan
中科院分区：
文献类型：
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作者：
Chuyuan Tao;Hunmin Kim;N. Hovakimyan
本文提出了一种基于最优采样的方法来解决静态和动态环境下的实时运动规划问题，利用快速探索随机树（RRT）算法和模型预测路径积分（MPPI）算法。RRT算法在MPPI算法中提供随机控制分布的标称平均值，从而在静态和动态环境中获得令人满意的控制性能，而无需进行精细的参数调整。我们还讨论了选择正确的平均值的MPPI算法，平衡探索和最优性差距，给定一个固定的样本量的重要性。特别地，需要足够大的均值来充分探索状态空间，并且需要足够小的均值来保证样本重构最优控制。所提出的方法自动化的过程中，选择正确的意思，将RRT算法。仿真结果表明，该算法可以解决静态或动态环境下的运动规划问题。
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.