在线计算安全可达集，让机器人运动规划更安全高效
Online, Reachability-Aware, Sampling-Based Motion Planning
- 用快速区间法在线生成可达集上界，保证安全性
- 实测安全事故减少99%以上，真实赛车无碰撞运行
- 无需预计算，可扩展至复杂系统，适合高动态场景
基于采样的模型预测控制（MPC）算法在多种机器人导航中具有灵活性。以往方法缺乏硬性安全保证，本文通过在线快速的区间化管道计算出保证的可达集上界，解决了这一问题。所提方法性能接近先进可达性规划器，但无需昂贵的预计算步骤，且可扩展至现有方法无法处理的系统。在赛车仿真中，安全违规减少超过99%；真实硬件实验中，成功控制模型赛车完成驾驶任务，未发生碰撞。
原文摘要 · Abstract (English)
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.
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