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Learning to Improve Multi-Robot Hallway Navigation.
Jin-Soo Park, Brian Tsang, Harel
Yedidsion, Garrett Warnell, Daehyun Kyoung, and Peter Stone.
In Proceedings of the 4th Conference on Robot Learning (CoRL),
November 2020.
Video presentation
As multi-robot applications become more prevalent, it becomes necessary to develop navigation systems which allow autonomous mobile robots to efficiently and safely pass each other in confined spaces. Existing navigation systems, such as the widely used ROS Navigation Stack, usually produce safe, collision free paths in static environments. However, these systems are not perfect, and when multiple mobile robots simultaneously navigate in narrow spaces, collisions and turnarounds are not uncommon. Fine-tuning and enhancing such navigation stacks is not as simple as it looks since they are made up of multiple layers of code, and there exists a tradeoff between optimizing for efficiency, i.e. minimizing time to destination (TTD) vs. optimizing for safety, i.e. minimizing collisions, with each objective leading to a different combination of parameter values. In this paper we develop a methodology to improve existing navigation stacks with regards to both objectives, without tuning their parameters, while preserving their inherent safety control properties. Our proposed approach is a decentralized learning-based approach that is geared toward real world robotic deployment, by requiring little computing resources. It is agnostic of the underlying navigation stack and can adapt to different types of environmental layouts (i.e., hallway structures).
@InProceedings{CoRL20-Park,
author = {Jin-Soo Park and Brian Tsang and Harel Yedidsion and Garrett Warnell and Daehyun Kyoung and Peter Stone},
title = {Learning to Improve Multi-Robot Hallway Navigation},
booktitle = {Proceedings of the 4th Conference on Robot Learning (CoRL)},
location = {Virtual Conference},
month = {November},
year = {2020},
wwwnote={<a href="https://youtu.be/i4QGXfNSJSo">Video presentation</a>},
abstract = {
As multi-robot applications become more prevalent, it becomes necessary to deve
lop navigation systems which allow autonomous mobile robots to efficiently and
safely pass each other in confined spaces. Existing navigation systems, such as
the widely used ROS Navigation Stack, usually produce safe, collision free path
s in static environments. However, these systems are not perfect, and when mult
iple mobile robots simultaneously navigate in narrow spaces, collisions and tur
narounds are not uncommon. Fine-tuning and enhancing such navigation stacks is
not as simple as it looks since they are made up of multiple layers of code, an
d there exists a tradeoff between optimizing for efficiency, i.e. minimizing ti
me to destination (TTD) vs. optimizing for safety, i.e. minimizing collisions,
with each objective leading to a different combination of parameter values. In
this paper we develop a methodology to improve existing navigation stacks with
regards to both objectives, without tuning their parameters, while preserving t
heir inherent safety control properties. Our proposed approach is a decentraliz
ed learning-based approach that is geared toward real world robotic deployment,
by requiring little computing resources. It is agnostic of the underlying navi
gation stack and can adapt to different types of environmental layouts (i.e., h
allway structures).
},
}
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