AI 中文总结
本文提出一种在线可达性感知的基于采样的运动规划方法，通过快速区间流水线计算可达集超近似，在无需预计算下达到先进性能，并显著降低安全违规。
AI 中文摘要
基于采样的模型预测控制（MPC）算法是一类灵活的控制器，用于各种机器人系统的导航。历史上，此类方法缺乏硬安全性保证，而我们在本工作中通过使用快速、基于区间的流水线在线计算保证的可达集超近似来弥补这一缺陷。我们表明，我们的方法在性能上与最先进的可达性规划器相当，且无需昂贵的预计算步骤，并可扩展到现有方法无法处理的系统。最后，我们证明我们的技术在赛车仿真中将安全违规减少了超过99%，并在真实硬件实验中成功控制模型赛车而无碰撞。
英文摘要
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.
Comments8 pages, 1 figure
