UAV path planning algorithm based on Deep Q-Learning to search for a floating lost target in the ocean

In the context of real world application, Search and Rescue Missions on the ocean surface remain a complex task due to the large-scale area and the forces of the ocean currents, spreading lost targets and debris in an unpredictable way. In this work, we present a Path Planning Approach to search for...

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Bibliographic Details
Published in:	Robotics and autonomous systems Vol. 179; p. 104730
Main Authors:	Boulares, Mehrez, Fehri, Afef, Jemni, Mohamed
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-09-2024
Subjects:	Ocean currents Reinforcement learning Search and Rescue (SAR) UAV UAV Search and Rescue (SAR) Reinforcement learning Ocean currents
Online Access:	Get full text
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Summary:	In the context of real world application, Search and Rescue Missions on the ocean surface remain a complex task due to the large-scale area and the forces of the ocean currents, spreading lost targets and debris in an unpredictable way. In this work, we present a Path Planning Approach to search for a lost target on ocean surface using a swarm of UAVs. The combination of GlobCurrent dataset and a Lagrangian simulator is used to determine where the particles are moved by the ocean currents forces while Deep Q-learning algorithm is applied to learn from their dynamics. The evaluation results of the trained models show that our search strategy is effective and efficient. Over a total search area (red Sea zone), surface of 453422 Km2, we have shown that our strategy Search Success Rate is 98.61%, the maximum Search Time to detection is 15 days and the average Search Time to detection is almost 15 h. •In this work, we present a Path Planning Approach to search for a lost target on ocean surface using a swarm of UAVs.•The combination of GlobCurrent dataset and a Lagrangian simulator is used to determine where the particles are moved by the ocean currents forces while Deep Q-learning algorithm is applied to learn from their dynamics.•The evaluation results of the trained models show that our search strategy is effective and efficient.•Over a total search area (red Sea zone), surface of 453422 Km2, we have shown that our strategy Search Success Rate is 98.61%, the maximum Search Time to detection is 15 days and the average Search Time to detection is almost 15 h.
ISSN:	0921-8890 1872-793X
DOI:	10.1016/j.robot.2024.104730