Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral (MPPI) control, have shown promise in addressing these complex motion planning problems. However, the performance of MPPI relies heavily on the choice of sampling distribution. Existing literature often uses the previously computed input sequence as the mean of a Gaussian distribution for sampling, leading to potential failures and local minima. In this paper, we propose a novel derivation of MPPI that allows for arbitrary sampling distributions to enhance efficiency, robustness, and convergence while alleviating the problem of local minima. We present an efficient importance sampling scheme that combines classical and learning-based ancillary controllers simultaneously, resulting in more informative sampling and control fusion. Several simulated and real-world demonstrate the validity of our approach.

翻译：自主机器人在动态环境中的运动规划因机器人动力学不确定性及与其他智能体的交互而面临诸多挑战。基于采样的模型预测控制方法，如模型预测路径积分（MPPI）控制，在解决这些复杂运动规划问题中展现出潜力。然而，MPPI的性能高度依赖于采样分布的选择。现有文献通常将先前计算的输入序列作为高斯分布的均值用于采样，这可能导致执行失败及陷入局部最优。本文提出一种新颖的MPPI推导方法，允许使用任意采样分布以提升效率、鲁棒性和收敛性，同时缓解局部最优问题。我们提出一种高效的重要性采样方案，能够同时融合经典与基于学习的辅助控制器，从而实现更具信息量的采样与控制融合。多组仿真与真实实验验证了本方法的有效性。