用信号时序逻辑保障机器人运动安全，同时保持高效规划。
Safety-aware Model Predictive Path Integral Control with Signal Temporal Logic
- 将STL约束转为时变控制屏障函数，嵌入采样式模型预测控制器
- 在4个火星车场景中实现高安全性和计算效率，优于多个基线方法
- 适用于复杂任务中需严格满足时空约束的机器人系统
安全感知的运动规划在机器人领域仍具挑战性，尤其在任务时间紧迫且规范复杂的场景下。本文提出一种名为 safety-aware-stl-mppi 的高效采样式递推时域规划框架，旨在促进满足以信号时序逻辑（STL）表达的约束。该方法将离散时间STL公式编码为时变控制屏障函数（CBF），并集成至模型预测路径积分（MPPI）控制器中。本方法兼具低计算成本与可并行化采样的优势，同时利用CBF确保STL约束的满足。我们在四个具有多样化环境与代价设置的火星车规划案例中对比多个MPPI基线，结果表明所提方法在安全性与效率上均表现优异。此外，还通过NVIDIA Isaac Lab平台完成了四旋翼飞行器的规划实验。
原文摘要 · Abstract (English)
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.
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