DRL-searcher: A unified approach to multi-robot efficient search for a moving target

Publication Type

Journal Article

Publication Date

5-2023

Abstract

This article studies the multirobot efficient search (MuRES) for a nonadversarial moving target problem, whose objective is usually defined as either minimizing the target’s expected capture time or maximizing the target’s capture probability within a given time budget. Different from canonical MuRES algorithms, which target only one specific objective, our proposed algorithm, named distributional reinforcement learning-based searcher (DRL-Searcher), serves as a unified solution to both MuRES objectives. DRL-Searcher employs distributional reinforcement learning (DRL) to evaluate the full distribution of a given search policy’s return, that is, the target’s capture time, and thereafter makes improvements with respect to the particularly specified objective. We further adapt DRL-Searcher to the use case without the target’s real-time location information, where only the probabilistic target belief (PTB) information is provided. Lastly, the recency reward is designed for implicit coordination among multiple robots. Comparative simulation results in a range of MuRES test environments show the superior performance of DRL-Searcher to state of the arts. Additionally, we deploy DRL-Searcher to a real multirobot system for moving target search in a self-constructed indoor environment with satisfying results.

Keywords

Budget control, Heuristic algorithms, Industrial robots, Learning algorithms, Modular robots, Multipurpose robots, Robot learning

Discipline

Artificial Intelligence and Robotics | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Neural Networks and Learning Systems

First Page

1

Last Page

14

ISSN

2162-237X

Identifier

10.1109/TNNLS.2023.3274667

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TNNLS.2023.3274667

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