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A Coverage Path Planning Algorithm for Self-Organizing Drone Swarms

Drone swarms are employed for multiple applications nowadays, such as early detection of forest fires, disaster response missions etc., area coverage being the main hurdle these applications face. In this work, an algorithm is proposed for drone swarms that achieves full area coverage and Point of Interest (PoI) detection with no decomposition for areas with simple polygon shapes and no obstacles. It employs small pieces of information that are exchanged among the swarm drones in real-time. Parallel lines and spiral coverage paths are examined in terms of the number of slowdowns in the swarms speed to fully collect information from a detected PoI, the scanned PoIs, and the formation switches that are executed when the swarm has to alter its flying direction. Simulations are developed for a square area shape. The results showcase that the parallel coverage path is more efficient in terms of cover time and travel distance compared to the spiral. The parallel coverage requires less slowdowns for the same percentage of scanned PoIs.