Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesize optimal controls on-the-fly. In practice, however, MPPI encounters problems limiting its application. For instance, it has been observed that MPPI tends to make poor decisions if unmodeled dynamics or environmental disturbances exist, preventing its use in safety-critical applications. Moreover, the multi-threaded simulations used by MPPI require significant onboard computational resources, making the algorithm inaccessible to robots without modern GPUs. To alleviate these issues, we propose a novel (Shield-MPPI) algorithm that provides robustness against unpredicted disturbances and achieves real-time planning using a much smaller number of parallel simulations on regular CPUs. The novel Shield-MPPI algorithm is tested on an aggressive autonomous racing platform both in simulation and using experiments. The results show that the proposed controller greatly reduces the number of constraint violations compared to state-of-the-art robust MPPI variants and stochastic MPC methods.

翻译：模型预测路径积分（MPPI）控制是一种基于采样的模型预测控制方法，通过模拟数千条轨迹并利用这些轨迹在线合成最优控制。然而，在实际应用中，MPPI会遇到限制其应用的问题。例如，当存在未建模动力学或环境扰动时，MPPI往往做出次优决策，从而阻碍其在安全关键场景中的应用。此外，MPPI使用的多线程模拟需要大量机载计算资源，使得该算法难以在没有现代GPU的机器人上使用。为解决这些问题，我们提出了一种新颖的（Shield-MPPI）算法，该算法能对未预测的扰动提供鲁棒性，并在普通CPU上通过显著减少并行模拟数量实现实时规划。新的Shield-MPPI算法在激进自主赛车平台上进行了仿真和实验测试。结果表明，与最先进的鲁棒MPPI变体和随机MPC方法相比，所提出的控制器大幅减少了约束违反次数。