Abstract
A swarm of unmanned vehicles can provide fine-grained spatial-temporal information acquisition and monitoring in comparison to a single agent, which is beneficial in terms of environment mapping, terrain exploration, and target hunting. However, the cooperation of single type of unmanned vehicles may be not qualified for fulfilling complex underwater tasks considering the motion constraints. In this paper, a joint design of the unmanned aerial/surface/underwater vehicle (UAV-USV-UUV) network, also referred to as 3U network, is proposed for cooperative underwater target hunting. We first introduce the advantages of this 3U heterogeneous system in multi-task cooperation and portray its system model. Moreover, we propose an energy-oriented target hunting model by jointly optimizing the UAV's position, the UUV's trajectory as well as their inter-connectivity. Finally, DQN algorithms are conceived to solve the proposed target hunting problem. Simulation results show the proposed scheme is suitable for underwater target hunting with a high success rate considering a trade-off between the system energy consumption and inter-connectivity.
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Original language | English |
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Pages (from-to) | 4085-4090 |
Number of pages | 6 |
Journal | IEEE Transactions on Vehicular Technology |
Volume | 72 |
Issue number | 3 |
Online published | 9 Nov 2022 |
DOIs | |
Publication status | Published - Mar 2023 |
Externally published | Yes |
Funding
The work of Jingjing Wang was supported by the National Natural Science Foundation of China under Grants 62071268 and 62222101. The work of Yong Ren was supported by the National Key R&D Program of China under Grant 2020YFD0901000. This work was supported in part by the Young Elite Scientist Sponsorship Program of the China Association foe Science and Technology under Grant 2020QNRC001.
Research Keywords
- cooperative target hunting
- deep Q-learning
- swarm intelligence
- Unmanned vehicles