TY - GEN
T1 - Toward autonomous mapping and exploration for mobile robots through deep supervised learning
AU - Bai, Shi
AU - Chen, Fanfei
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - We consider an autonomous mapping and exploration problem in which a range-sensing mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to yield the maximum information gain within its current field of view. We propose a novel and time-efficient approach to predict the most informative sensing action using a deep neural network. After training the deep neural network on a series of thousands of randomly-generated 'dungeon maps', the predicted optimal sensing action can be computed in constant time, with prospects for appealing scalability in the testing phase to higher dimensional systems. We evaluated the performance of deep neural networks on the autonomous exploration of two-dimensional workspaces, comparing several different neural networks that were selected due to their success in recent ImageNet challenges. Our computational results demonstrate that the proposed method provides high efficiency as well as accuracy in selecting informative sensing actions that support autonomous mobile robot exploration.
AB - We consider an autonomous mapping and exploration problem in which a range-sensing mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to yield the maximum information gain within its current field of view. We propose a novel and time-efficient approach to predict the most informative sensing action using a deep neural network. After training the deep neural network on a series of thousands of randomly-generated 'dungeon maps', the predicted optimal sensing action can be computed in constant time, with prospects for appealing scalability in the testing phase to higher dimensional systems. We evaluated the performance of deep neural networks on the autonomous exploration of two-dimensional workspaces, comparing several different neural networks that were selected due to their success in recent ImageNet challenges. Our computational results demonstrate that the proposed method provides high efficiency as well as accuracy in selecting informative sensing actions that support autonomous mobile robot exploration.
UR - http://www.scopus.com/inward/record.url?scp=85041955815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041955815&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8206050
DO - 10.1109/IROS.2017.8206050
M3 - Conference contribution
AN - SCOPUS:85041955815
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2379
EP - 2384
BT - IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
T2 - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Y2 - 24 September 2017 through 28 September 2017
ER -