Routing for a Network of Drones with the Aim of Search and Rescue

Network routing of drones for search and rescue operations is a critical challenge. This challenge arises due to the physical limitations of drones, adverse environmental conditions, and time constraints. In this paper, a novel approach for network routing of drones using the Q-Learning algorithm is proposed. This algorithm enables drones to automatically determine optimal paths in complex environments and adapt to environmental changes. Simulation results demonstrate that the Q-Learning algorithm can find shorter and more efficient routes compared to genetic algorithms. These findings highlight Q-Learning as a promising method for improving network routing of drones in search and rescue operations