Learning Local Planners for Human-aware Navigation in Indoor Environments
Ronja Guldenring, Michael Görner, Norman Hendrich, Niels Jul Jacobsen, Jianwei Zhang
Abstract
Established indoor robot navigation frameworks build on the separation between global and local planners. Whereas global planners rely on traditional graph search algorithms, local planners are expected to handle driving dynamics and resolve minor conflicts. We present a system to train neural-network policies for such a local planner component, explicitly accounting for humans navigating the space. DRL-agents are trained in randomized virtual 2D environments with simulated human interaction. The trained agents can be deployed as a drop-in replacement for other local planners and significantly improve on traditional implementations. Performance is demonstrated on a MiR-100 transport robot.
BibTeX
@inproceedings{iros2020_learninglocalpla,
title = {Learning Local Planners for Human-aware Navigation in Indoor Environments},
author = {Ronja Guldenring and Michael Görner and Norman Hendrich and Niels Jul Jacobsen and Jianwei Zhang},
booktitle = {IROS 2020},
year = {2020}
}