SAFER：基于强化学习的聚焦且高效的轨迹搜索实现安全避碰
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning
AI总结：
本文提出SAFER避碰系统，结合强化学习、在线轨迹规划与自动紧急制动，通过学习修正控制动作来聚焦搜索无碰撞轨迹，在真实环境中实现了更安全、高效且平滑的机器人控制。
摘要：
避碰是移动机器人和智能体在现实世界中安全运行的关键。在本研究中，我们提出了SAFER，这是一种高效且有效的避碰系统，能够通过修正操作员发送的控制指令来提升安全性。它结合了真实世界强化学习（RL）、基于搜索的在线轨迹规划以及自动紧急干预（例如自动紧急制动，AEB）。RL的目标是学习一种有效的修正控制动作，用于聚焦搜索无碰撞轨迹，并降低触发自动紧急制动的频率。这种新颖的设置使RL策略能够在真实室内环境中的移动机器人上安全且直接地进行学习，即使在训练期间也能将实际碰撞降至最低。我们的真实世界实验表明，与多个基线相比，我们的方法具有更高的平均速度、更低的碰撞率、更少的紧急干预、更小的计算开销以及更平滑的整体控制。
英文摘要：
Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcting the control commands sent by an operator. It combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn an effective corrective control action that is used in a focused search for collision-free trajectories, and to reduce the frequency of triggering automatic emergency braking. This novel setup enables the RL policy to learn safely and directly on mobile robots in a real-world indoor environment, minimizing actual crashes even during training. Our real-world experiments show that, when compared with several baselines, our approach enjoys a higher average speed, lower crash rate, less emergency intervention, smaller computation overhead, and smoother overall control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
