Consider a mobile robot tasked with localizing targets at unknown locations by obtaining relative measurements. The observations can be bearing or range measurements. How should the robot move so as to localize the targets and minimize the uncertainty in their locations as quickly as possible? This is a difficult optimization problem for which existing approaches are either greedy in nature or they rely on accurate initial estimates. We formulate this path planning problem as a reinforcement learning problem where the measurements are aggregated using a Bayesian histogram filter. The robot learns to minimize the total uncertainty of each target in the shortest amount of time using the current measurement and an aggregate representation of the current belief state. We analyze our method in a series of experiments where we show that our method outperforms a standard greedy approach. In addition, its performance is comparable to that of an offline algorithm which has access to the true location of the targets (More information available at https://ksengin.github.io/active-target-localization/ ).
|Original language||English (US)|
|Title of host publication||Springer Proceedings in Advanced Robotics|
|Publisher||Springer Science and Business Media B.V.|
|Number of pages||16|
|State||Published - 2021|
|Name||Springer Proceedings in Advanced Robotics|
Bibliographical noteFunding Information:
Acknowledgement. We thank the reviewers for their valuable comments and feedback. This work was supported by a Minnesota LCCMR grant and the NSF grant #1617718.
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.