Autonomous exploration under uncertainty via deep reinforcement learning on graphs

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 - https://www.scopus.com/pages/publications/85102404320

UR - https://www.scopus.com/pages/publications/85102404320#tab=citedBy

U2 - 10.1109/IROS45743.2020.9341657

DO - 10.1109/IROS45743.2020.9341657

M3 - Conference contribution

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020

Y2 - 24 October 2020 through 24 January 2021

ER -