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

TL;DR

SAFER is a collision avoidance system for mobile robots that combines reinforcement learning, online trajectory search, and emergency intervention to improve safety, efficiency, and reduce crashes in real-world indoor environments.

Contribution

This work introduces SAFER, a novel integrated system that uses RL and search-based planning for safe, efficient collision avoidance with real-world training capabilities.

Findings

Higher average speed compared to baselines

Lower crash rate and emergency interventions

Reduced computational overhead and smoother control

Abstract

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.