Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithms

Aerospace researchers used genetic algorithms to solve the problems of cooperative task assignment and path planning of multiple unmanned aerial vehicles (UAV) that are used to carry out special military operations. The researchers used one of the mission scenarios of the Suppression of Enemy Air Defense (SEAD) military operations. It was demonstrated that cooperative task assignment and path planning of multiple UAVs plays a key role in the SEAD military operations. They also focused on primary terrain information, used by the SEAD team to carry out cooperative military operations, such as location of targets, obstacles, and dangerous areas. The investigations also revealed that the timing constrains for simultaneous attacks and multiple tasks with specific time delays were applied for carrying more advanced military operations. Genetic algorithms was used to solve the problem, as it not restricted to continuity, differentiability, or unimodality of searching for the optimal solution.