Open AccessSpatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search
Author(s) -
Deyan Zheng,
Chunhui Liu,
Lizhen Huang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2187/1/012047
Subject(s) - computer science , task (project management) , reinforcement learning , field (mathematics) , real time computing , motion planning , route planning , algorithm , artificial intelligence , computer engineering , mathematical optimization , engineering , mathematics , systems engineering , robot , pure mathematics
A spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search is proposed in this paper, named STC-MARL, which is based on multi-agent reinforcement learning and allows several UAVs equipped with two types of sensors to learn to complete full view of a field of interest (FOI). The proposed STC-MARL algorithm can make participating UAVs to learn from the environment to complete the full coverage of a specific FOI while minimizing field of views (FOVs) interacted with each other. The experimental results show in detail with simulation that the UAVs in the mission can effectively accomplish the task of spatio-temporal coverage planning.