Heterogeneous UAV swarm system for target search in adversarial environment

Unmanned Aerial Systems (UAS) have great potential to aid in search and situation assessment. Here, we present a UAV swarm system performing target search in adversarial environment. It utilizes a Ant-Colony (ACO) and Artificial Potential Function (APF) based decentralised target search strategy. ACO meta-heuristic forms the higher level guidance algorithm and APF provide global and local path planning. Uncertainty maps are used to represent probable target locations. The algorithm is scalable and is shown to be robust to agent loss. Its distributed nature makes it ideal for applications in large scale search operations. Trajectory estimates are factored into prioritization resulting into better target selection and faster search.