5 research outputs found
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning
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.Comment: Accepted in IEEE International Conference on Automation Science and
Engineering (CASE), 202
Human Following in Mobile Platforms with Person Re-Identification
Human following is a crucial feature of human-robot interaction, yet it poses
numerous challenges to mobile agents in real-world scenarios. Some major
hurdles are that the target person may be in a crowd, obstructed by others, or
facing away from the agent. To tackle these challenges, we present a novel
person re-identification module composed of three parts: a 360-degree visual
registration, a neural-based person re-identification using human faces and
torsos, and a motion tracker that records and predicts the target person's
future position. Our human-following system also addresses other challenges,
including identifying fast-moving targets with low latency, searching for
targets that move out of the camera's sight, collision avoidance, and
adaptively choosing different following mechanisms based on the distance
between the target person and the mobile agent. Extensive experiments show that
our proposed person re-identification module significantly enhances the
human-following feature compared to other baseline variants