TY - GEN
T1 - Self-learning exploration and mapping for mobile robots via deep reinforcement learning
AU - Chen, Fanfei
AU - Bai, Shi
AU - Shan, Tixiao
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2019, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Mapping and exploration of a priori unknown environments is a crucial capability for mobile robot autonomy. A state-of-the-art approach for mobile robots equipped with range sensors uses mutual information as the basis for a cost metric [5], [14], and reasons about how much information gain is associated with each action a robot can take while constructing an occupancy map from its range measurements. However, the computational cost of such an optimization scales poorly as the number of potential robot actions increases. We propose a novel approach to utilize the local structure of the environment while predicting a robot’s optimal sensing action using Deep Reinforcement Learning (DRL) [19]. The learned exploration policy can select an optimal or near-optimal exploratory sensing action with improved computational efficiency. Our computational results demonstrate that the proposed method provides both efficiency and accuracy in choosing informative sensing actions.
AB - Mapping and exploration of a priori unknown environments is a crucial capability for mobile robot autonomy. A state-of-the-art approach for mobile robots equipped with range sensors uses mutual information as the basis for a cost metric [5], [14], and reasons about how much information gain is associated with each action a robot can take while constructing an occupancy map from its range measurements. However, the computational cost of such an optimization scales poorly as the number of potential robot actions increases. We propose a novel approach to utilize the local structure of the environment while predicting a robot’s optimal sensing action using Deep Reinforcement Learning (DRL) [19]. The learned exploration policy can select an optimal or near-optimal exploratory sensing action with improved computational efficiency. Our computational results demonstrate that the proposed method provides both efficiency and accuracy in choosing informative sensing actions.
UR - http://www.scopus.com/inward/record.url?scp=85083941432&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083941432&partnerID=8YFLogxK
U2 - 10.2514/6.2019-0396
DO - 10.2514/6.2019-0396
M3 - Conference contribution
AN - SCOPUS:85083941432
SN - 9781624105784
T3 - AIAA Scitech 2019 Forum
BT - AIAA Scitech 2019 Forum
T2 - AIAA Scitech Forum, 2019
Y2 - 7 January 2019 through 11 January 2019
ER -