The classical Model Predictive Path Integral (MPPI) control framework lacks reliable safety guarantees since it relies on a risk-neutral trajectory evaluation technique, which can present challenges for safety-critical applications such as autonomous driving. Additionally, if the majority of MPPI sampled trajectories concentrate in high-cost regions, it may generate an infeasible control sequence. To address this challenge, we propose the U-MPPI control strategy, a novel methodology that can effectively manage system uncertainties while integrating a more efficient trajectory sampling strategy. The core concept is to leverage the Unscented Transform (UT) to propagate not only the mean but also the covariance of the system dynamics, going beyond the traditional MPPI method. As a result, it introduces a novel and more efficient trajectory sampling strategy, significantly enhancing state-space exploration and ultimately reducing the risk of being trapped in local minima. Furthermore, by leveraging the uncertainty information provided by UT, we incorporate a risk-sensitive cost function that explicitly accounts for risk or uncertainty throughout the trajectory evaluation process, resulting in a more resilient control system capable of handling uncertain conditions. By conducting extensive simulations of 2D aggressive autonomous navigation in both known and unknown cluttered environments, we verify the efficiency and robustness of our proposed U-MPPI control strategy compared to the baseline MPPI. We further validate the practicality of U-MPPI through real-world demonstrations in unknown cluttered environments, showcasing its superior ability to incorporate both the UT and local costmap into the optimization problem without introducing additional complexity.

翻译：经典模型预测路径积分（MPPI）控制框架缺乏可靠的安全保障，因其依赖于风险中性的轨迹评估技术，这在自动驾驶等安全关键型应用中可能带来挑战。此外，若MPPI采样轨迹大多集中于高成本区域，则可能生成不可行的控制序列。为解决此问题，我们提出U-MPPI控制策略，这是一种能够有效管理系统不确定性并整合更高效轨迹采样策略的新方法。其核心思想是利用无迹变换（UT）传播系统动力学的均值与协方差，超越传统MPPI方法。由此，我们引入一种新颖且更高效的轨迹采样策略，显著增强状态空间探索，最终降低陷入局部极小值的风险。进一步，借助UT提供的不确定性信息，我们纳入风险敏感型代价函数，在轨迹评估过程中明确考虑风险或不确定性，从而构建出能够应对不确定条件的鲁棒控制系统。通过在已知和未知杂乱环境中进行二维激进自主导航的广泛仿真，我们验证了所提U-MPPI控制策略相比基线MPPI的效力和鲁棒性。我们还通过未知杂乱环境中的实物演示进一步验证了U-MPPI的实用性，展示了其在无需增加额外复杂度的情况下，将UT与局部代价地图整合到优化问题中的卓越能力。