让机器人实时生成安全的常规与应急双预案
Contingency Constrained Planning with MPPI within MPPI
- 在常规规划中嵌入应急计划，统一优化
- 实测可在移动机器人上实时生成双预案
- 适合需高安全性的自主系统应用
为确保安全，自主系统必须能应对突发变化并适时执行应急计划。现有方法要么平衡常规与应急行为，要么仅规划单一应急方案，但无法保证全程安全。本文提出Contingency-MPPI，一种将应急规划嵌入常规规划器的数据驱动优化策略。通过自适应重要性采样学习最优应急约束控制序列，并利用轻量级路径规划器和轨迹优化器初始化，提升采样效率。最后，仿真与硬件实验表明，该算法可在移动机器人上实时生成常规与应急计划。
原文摘要 · Abstract (English)
For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance between nominal and contingency behavior, or plan for a singular contingency plan; however, this does not guarantee that the resulting plan is safe for all time. To address this research gap, this paper presents Contingency-MPPI, a data-driven optimization-based strategy that embeds contingency planning inside a nominal planner. By learning to approximate the optimal contingency-constrained control sequence with adaptive importance sampling, the proposed method's sampling efficiency is further improved with initializations from a lightweight path planner and trajectory optimizer. Finally, we present simulated and hardware experiments demonstrating our algorithm generating nominal and contingency plans in real time on a mobile robot.
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