基于采样的强化避障算法，提升动态不确定环境下的导航可靠性。
Chance-Constrained Sampling-Based MPC for Collision Avoidance in Uncertain Dynamic Environments
- 直接使用非线性概率约束，避免系统动力学线性化
- 通过分层障碍物建模与确定性约束形式提升计算效率
- 适合需要实时避障的机器人或自动驾驶场景
在感知与运动存在不确定性的动态环境中安全导航极具挑战。本文提出一种基于机会约束的无迹模型预测路径积分（C2U-MPPI）框架，通过集成无迹采样策略与概率机会约束，实现对不确定性环境的鲁棒、高效控制。与基于梯度的MPC方法不同，本方法（i）不依赖系统动力学线性化，直接处理非凸和非线性机会约束，提升优化精度与灵活性；（ii）通过采用确定性形式的概率约束与分层动态障碍物表示，显著提升计算效率，支持多障碍物实时处理。在仿真及真实人机共享环境中的大量实验验证了该算法的有效性，其生成的轨迹与控制输入均满足系统动力学与约束条件，得益于无迹采样与风险敏感的轨迹评估机制。补充视频见：https://youtu.be/FptAhvJlQm8。
原文摘要 · Abstract (English)
Navigating safely in dynamic and uncertain environments is challenging due to uncertainties in perception and motion. This letter presents the Chance-Constrained Unscented Model Predictive Path Integral (C2U-MPPI) framework, a robust sampling-based Model Predictive Control (MPC) algorithm that addresses these challenges by leveraging the U-MPPI control strategy with integrated probabilistic chance constraints, enabling more reliable and efficient navigation under uncertainty. Unlike gradient-based MPC methods, our approach (i) avoids linearization of system dynamics by directly applying non-convex and nonlinear chance constraints, enabling more accurate and flexible optimization, and (ii) enhances computational efficiency by leveraging a deterministic form of probabilistic constraints and employing a layered dynamic obstacle representation, enabling real-time handling of multiple obstacles. Extensive experiments in simulated and real-world human-shared environments validate the effectiveness of our algorithm against baseline methods, showcasing its capability to generate feasible trajectories and control inputs that adhere to system dynamics and constraints in dynamic settings, enabled by unscented-based sampling strategy and risk-sensitive trajectory evaluation. A supplementary video is available at: https://youtu.be/FptAhvJlQm8.
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