Evolutionary Utility Prediction Matrix-Based Mission Planning for Unmanned Aerial Vehicles in Complex Urban Environments

An unmanned aerial vehicle (UAV) swarm that self-organizes to provide superior intelligence and overwhelming effects is a promising technology in urban scenarios. Complex terrain constraints of urban environment increase mission planning difficulties, and bring a strong input-output coupling between subproblems of mission planning, which affect the computing performance and effectiveness of the UAV swarm. In this paper, an evolutionary utility prediction matrix (EUPM) method is presented to solve the input-output-coupled mission planning problem for a UAV swarm executing heterogeneous tasks in an urban scenario with complex constraints. A structured framework of urban missions is established with a distributed mission planning architecture. The input-output coupling relationships between the subproblems of mission planning are analyzed in terms of typical urban mission patterns. Modules for the mission planning subproblems are designed with improved input-output coupling relationships. Simulations and hardware-in-loop experiments demonstrate that the EUPM method achieves accurate prediction of the utility and high effective mission planning solutions than the traditional methods.