针对随机混合系统，提出风险感知路径规划方法，提升机器人避障效率。
Risk-aware MPPI for Stochastic Hybrid Systems
- 结合无迹变换捕捉状态与切换面的随机性，优于仅依赖均值的切换策略。
- 在模拟环境中，机器人利用人类动态模型时收敛更快且无碰撞。
- 适合需预测动态障碍物行为的自主导航场景，如人机共存环境。
随机混合系统的路径规划面临独特挑战：需预测受状态依赖动力学切换函数影响的未来状态分布。本文提出一种模型预测路径积分控制（MPPI）的变体，用于此类系统的运动规划。当样本数量较少时，蒙特卡洛方法在状态依赖扰动下预测未来状态可能不准确。为此，我们采用近期提出的基于无迹变换的方法，以捕捉状态及状态依赖切换面中的随机性。这与以往仅基于预测状态均值进行切换的做法形成对比。本研究聚焦于移动机器人在具有传感器约束注意力区的动态代理存在下的导航问题。在模拟移动机器人上评估框架，结果表明，当机器人利用混合人类动力学时，相比未使用该模型的情况，能更快收敛至目标且无碰撞。
原文摘要 · Abstract (English)
Path Planning for stochastic hybrid systems presents a unique challenge of predicting distributions of future states subject to a state-dependent dynamics switching function. In this work, we propose a variant of Model Predictive Path Integral Control (MPPI) to plan kinodynamic paths for such systems. Monte Carlo may be inaccurate when few samples are chosen to predict future states under state-dependent disturbances. We employ recently proposed Unscented Transform-based methods to capture stochasticity in the states as well as the state-dependent switching surfaces. This is in contrast to previous works that perform switching based only on the mean of predicted states. We focus our motion planning application on the navigation of a mobile robot in the presence of dynamically moving agents whose responses are based on sensor-constrained attention zones. We evaluate our framework on a simulated mobile robot and show faster convergence to a goal without collisions when the robot exploits the hybrid human dynamics versus when it does not.
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