Zhou, X;
Wu, P;
Zhang, H;
Guo, W;
Liu, Y;
(2019)
Learn to navigate: cooperative path planning for unmanned surface vehicles using deep reinforcement learning.
IEEE Access
, 7
pp. 165262-165278.
10.1109/ACCESS.2019.2953326.
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Abstract
Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK.
| Type: | Article |
|---|---|
| Title: | Learn to navigate: cooperative path planning for unmanned surface vehicles using deep reinforcement learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ACCESS.2019.2953326 |
| Publisher version: | https://doi.org/10.1109/ACCESS.2019.2953326 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Deep reinforcement learning , motion planning , multi-agent systems , unmanned surface vehicles (USVs) , USV formations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10086087 |
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