Application of Thompson sampling on multi-target search

Zhaolin Wang; Yangyang Gao; Qirui Xiao; Qizheng Zhang

doi:10.1117/12.2639499

17 May 2022 Application of Thompson sampling on multi-target search

Zhaolin Wang, Yangyang Gao, Qirui Xiao, Qizheng Zhang

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122591Q (2022) https://doi.org/10.1117/12.2639499
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

Multi-robot coordination demonstrates outstanding performance in exploring and tracking targets in unknown environments, especially in complex terrain or disaster environments. In these cases, the distribution of the search area is changeable and irregular, and the exploration target may also move in an unknown environment. To quickly determine the location of these exploration targets, we propose a distributed control strategy in the article, which applies Bernoulli Thompson sampling to solve the multi-armed bandit problem to allow multiple robots to share information and efficiently search targets. This algorithm significantly speeds up the robot's search for targets, especially when most targets are concentrated in certain fixed areas of the environment.

Citation Download Citation

Zhaolin Wang, Yangyang Gao, Qirui Xiao, and Qizheng Zhang "Application of Thompson sampling on multi-target search", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122591Q (17 May 2022); https://doi.org/10.1117/12.2639499

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available