发表机构
University of Southern California; University of Michigan; Toyota Motor North America R&D; ETH Zürich（南加州大学; 密歇根大学; 丰田北美研发中心; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出safety-aware-stl-mppi框架，将STL公式编码为CBF并集成到MPPI控制器，经火星车与四旋翼实验验证，可在复杂约束下实现高安全与效率的运动规划。
AI 中文摘要
感知安全的运动规划仍是机器人领域的一项挑战，尤其是在任务时间紧迫且受复杂规范约束的情况下。本文提出了safety-aware-stl-mppi，这是一种计算高效的基于采样的滚动时域规划框架，旨在提升对用信号时序逻辑（Signal Temporal Logic，STL）表述的约束的满足度。我们的方法将离散时间STL公式编码为候选时变控制障碍函数（Control Barrier Functions，CBF），并将其集成到模型预测路径积分（Model Predictive Path Integral，MPPI）控制器中。该方法继承了可高效并行化的基于采样的规划器的低计算成本优势，同时利用CBF处理STL表述的约束。我们针对四个具有多样环境和成本设置的人工火星车规划案例研究，与多个MPPI基准进行对比，结果显示我们的方法始终能实现高安全性和效率。此外，我们还展示了在NVIDIA Isaac Lab平台上进行的四旋翼飞行器规划实验。
英文摘要
Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-aware-stl-mppi, a computationally efficient sampling-based receding-horizon planning framework designed to promote satisfaction of constraints expressed in Signal Temporal Logic (STL). Our approach encodes discrete-time STL formulas into candidate time-varying control barrier functions (CBF), which are integrated into a model predictive path integral (MPPI) controller. Our method inherits the benefits of low computational cost from an efficiently parallelizable sampling based planner and utilizes CBF for constraints expressed in STL. We compare against several MPPI baselines using four artificial Mars Rover planning case studies with a diverse environment and cost setups, where we show our method consistently achieving high safety and efficiency. We show a quadcopter planning experiment with NVIDIA Isaac Lab.
