Problem
Research questions and friction points this paper is trying to address.
Sampling-Based Model-Predictive Control
safety guarantees
reachable-set overapproximations
Online, Reachability-Aware, Sampling-Based Motion Planning
🤖 AI Summary
本文提出了一种在线可达性感知的采样基运动规划方法，通过快速区间管道计算可达集近似，无需预计算步骤，减少了99%以上的安全违规。
📝 Abstract
Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations online with a fast, interval-based pipeline. We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step, and can be scaled to systems that are infeasible using existing approaches. Finally, we demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Online Reachability
Sampling-Based MPC
Interval-based Pipeline
Safety Guarantees
Real Hardware Experiments
🔎 Similar Papers
No similar papers found.
