This paper introduces a novel nonlinear stochastic model predictive control path integral (MPPI) method, which considers chance constraints on system states. The proposed belief-space stochastic MPPI (BSS-MPPI) applies Monte-Carlo sampling to evaluate state distributions resulting from underlying systematic disturbances, and utilizes a Control Barrier Function (CBF) inspired heuristic in belief space to fulfill the specified chance constraints. Compared to several previous stochastic predictive control methods, our approach applies to general nonlinear dynamics without requiring the computationally expensive system linearization step. Moreover, the BSS-MPPI controller can solve optimization problems without limiting the form of the objective function and chance constraints. By multi-threading the sampling process using a GPU, we can achieve fast real-time planning for time- and safety-critical tasks such as autonomous racing. Our results on a realistic race-car simulation study show significant reductions in constraint violation compared to some of the prior MPPI approaches, while being comparable in computation times.

翻译：本文提出了一种新颖的非线性随机模型预测控制路径积分方法，该方法考虑了系统状态的机会约束。所提出的信念空间随机MPPI方法采用蒙特卡洛采样来评估由底层系统扰动导致的状态分布，并利用信念空间中受控制屏障函数启发的启发式方法来实现指定的机会约束。与先前几种随机预测控制方法相比，我们的方法适用于一般非线性动力学系统，无需进行计算成本高昂的系统线性化步骤。此外，BSS-MPPI控制器能够求解优化问题，而不限制目标函数和机会约束的形式。通过使用GPU对采样过程进行多线程处理，我们能够为自动驾驶赛车等时间与安全关键任务实现快速的实时规划。我们在真实赛车仿真研究中的结果表明，与一些先前的MPPI方法相比，该方法在约束违反方面显著减少，同时计算时间相当。