TY - GEN
T1 - Information-theoretic exploration with Bayesian optimization
AU - Bai, Shi
AU - Wang, Jinkun
AU - Chen, Fanfei
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - We consider an autonomous exploration problem in which a 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 be most informative within its current field of view. We propose a novel approach to predict mutual information (MI) using Bayesian optimization. Over several iterations, candidate sensing actions are suggested by Bayesian optimization and added to a committee that repeatedly trains a Gaussian process (GP). The GP estimates MI throughout the robot's action space, serving as the basis for an acquisition function used to select the next candidate. The best sensing action in the committee is executed by the robot. This approach is compared over several environments with two batch methods, one which chooses the most informative action from a set of pseudorandom samples whose MI is explicitly evaluated, and one that applies GP regression to this sample set. Our computational results demonstrate that the proposed method provides not only computational efficiency and rapid map entropy reduction, but also robustness in comparison with competing approaches.
AB - We consider an autonomous exploration problem in which a 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 be most informative within its current field of view. We propose a novel approach to predict mutual information (MI) using Bayesian optimization. Over several iterations, candidate sensing actions are suggested by Bayesian optimization and added to a committee that repeatedly trains a Gaussian process (GP). The GP estimates MI throughout the robot's action space, serving as the basis for an acquisition function used to select the next candidate. The best sensing action in the committee is executed by the robot. This approach is compared over several environments with two batch methods, one which chooses the most informative action from a set of pseudorandom samples whose MI is explicitly evaluated, and one that applies GP regression to this sample set. Our computational results demonstrate that the proposed method provides not only computational efficiency and rapid map entropy reduction, but also robustness in comparison with competing approaches.
UR - http://www.scopus.com/inward/record.url?scp=85006372438&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2016.7759289
DO - 10.1109/IROS.2016.7759289
M3 - Conference contribution
AN - SCOPUS:85006372438
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 1816
EP - 1822
BT - IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
T2 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Y2 - 9 October 2016 through 14 October 2016
ER -