Adding Boulares article

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       <h1>Peer-reviewed articles using Parcels</h1>
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+                UAV path planning algorithm based on Deep Q-Learning to search for a floating lost target in the ocean
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+              Boulares, M, A Fehri, M Jemni (2024),
+              <i>Robotics and Autonomous Systems</i>, <i>in press</i>.<br />
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+                >[ Expand abstract ]</a
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+              <a
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+                href="https://doi.org/10.1016/j.robot.2024.104730"
+                ><i class="fa fa-external-link fa-1x"></i> [ Link to article ]</a
+              >
+              <span class="pubnum">175</span>
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+                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 Km, 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.
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