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
Citation
GUO, Hongliang; PENG, Qihang; CAO, Zhiguang; and JIN, Yaochu.
DRL-searcher: A unified approach to multi-robot efficient search for a moving target. (2023). IEEE Transactions on Neural Networks and Learning Systems. 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/8217
Copyright Owner and License
Authors
Additional URL
https://doi.org/10.1109/TNNLS.2023.3274667