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 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${}^2$, 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.