Evolutionary Optimization-based Mission Planning for UAS Traffic Management (UTM)

In this work, we propose a route planning scheme for large scale Unmanned Aircraft System (UAS) or multiple-drone operations in complex urban air space. The route planning system consists of a path finding algorithm and a scheduling system for safe and efficient uses of airspace. The work flow of the system is: first, generate shortest paths between origination and destination points via heuristic search and second, schedule the submitted flights to avoid possible conflicts. The scheduling process is performed by Evolutionary Algorithm (EA). Such a system would postpone or delay flight requests that may collide with previous flights and reject the conflicted flights. We also propose a fitness function for the EA to minimize both the total delay of the flight requests and the possibility of UAS collision. To demonstrate the feasibility of the proposed route planning models, preliminary simulations in a local town in Singapore are presented and discussed.