Safety-aware Model Predictive Path Integral Control with Signal Temporal Logic

2026-08-24

AI Summary

本文提出了一种基于信号时序逻辑的安全感知模型预测路径积分控制方法，通过将STL约束编码为控制屏障函数，并结合MPPI控制器，以提高机器人在复杂任务中的安全性和效率。

Abstract

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.

Problem

• Safety-aware motion planning
• Signal Temporal Logic
• Robotics

Innovation

• Safety-aware Motion Planning
• Signal Temporal Logic (STL)
• Control Barrier Functions (CBF)
• Model Predictive Path Integral (MPPI)