SAFER: Safe Collision Avoidance Using Focused and Efficient Trajectory Search with Reinforcement Learning

• Published in: 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) pp. 1 - 8
• Main Authors: Srouji, Mario, Thomas, Hugues, Tsai, Yao-Hung Hubert, Farhadi, Ali, Zhang, Jian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.08.2023
• Subjects: Computer crashes; Reinforcement learning; Safety; Search methods; Training; Trajectory; Trajectory planning
• ISSN: 2161-8089
• DOI: 10.1109/CASE56687.2023.10260402

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.