Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs

• URL: http://arxiv.org/abs/2007.12640v1
• Date: Fri, 24 Jul 2020 16:50:41 GMT
• Title: Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
• Authors: Fanfei Chen, John D. Martin, Yewei Huang, Jinkun Wang, Brendan Englot
• Abstract summary: 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. 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.
• Score: 5.043563227694137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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