TY - GEN
T1 - Autonomous exploration under uncertainty via deep reinforcement learning on graphs
AU - Chen, Fanfei
AU - Martin, John D.
AU - Huang, Yewei
AU - Wang, Jinkun
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently in real-time; it must choose sensing actions that both curb localization uncertainty and achieve information gain. For this problem, belief space planning methods that forward- simulate robot sensing and estimation may often fail in real-time implementation, scaling poorly with increasing size of the state, belief and action spaces. We propose a novel approach that uses graph neural networks (GNNs) in conjunction with deep reinforcement learning (DRL), enabling decision-making over graphs containing exploration information to predict a robot's optimal sensing action in belief space. The policy, which is trained in different random environments without human intervention, offers a real-time, scalable decision-making process whose high-performance exploratory sensing actions yield accurate maps and high rates of information gain.
AB - We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently in real-time; it must choose sensing actions that both curb localization uncertainty and achieve information gain. For this problem, belief space planning methods that forward- simulate robot sensing and estimation may often fail in real-time implementation, scaling poorly with increasing size of the state, belief and action spaces. We propose a novel approach that uses graph neural networks (GNNs) in conjunction with deep reinforcement learning (DRL), enabling decision-making over graphs containing exploration information to predict a robot's optimal sensing action in belief space. The policy, which is trained in different random environments without human intervention, offers a real-time, scalable decision-making process whose high-performance exploratory sensing actions yield accurate maps and high rates of information gain.
UR - http://www.scopus.com/inward/record.url?scp=85102404320&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102404320&partnerID=8YFLogxK
U2 - 10.1109/IROS45743.2020.9341657
DO - 10.1109/IROS45743.2020.9341657
M3 - Conference contribution
AN - SCOPUS:85102404320
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6140
EP - 6147
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -