GHOST and 3GEA: Task-driven Coalition and Planning Algorithms for Smart Airborne Vehicles

Intelligent unmanned aerial vehicles (UAVs) can effortlessly execute large-scale and complex missions due to their maneuverability and autonomy. Networks of heterogenous UAVs carrying various equipment and resources offer more opportunities to execute the tasks that single UAV may fail to do it alone as multiple UAVs can form coalitions and cooperatively share their resources and complete the missions. In this paper, two novel algorithms have been proposed to tackle the challenges that engulf the problem of distributed task allocation and coalition formation with multiple vehicles. A dynamic game-theory-based algorithm named GHOST where the UAVs autonomously act as rational players and move according to their preferences to choose the members and sort the tasks for their coalitions. In addition to an evolutionary algorithm with 3-generations (3GEA) used for planning the coordinates of UAVs. This algorithm makes use of an archive of previous best solutions and a shallow FNN (feedforward neural network) trained with multiple supervised algorithms to improve the convergence and diversity of solutions. The comparative analyses with more than 20 state-of-the-art clustering and evolutionary algorithms proved that the proposed algorithms could achieve optimal coalition structures and complete missions with a success rate of 85-100%.