An ETA-Based Tactical Conflict Resolution Method for Air Logistics Transportation

Air logistics transportation has become one of the most promising markets for civil drone industry. However, large flow, high density, and complex environmental characteristics urban scenes make tactical conflict resolution very challenging. Existing methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets fail to take temporal constraints pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based method air is designed reconstructing state space following risk sectors concept through use Estimated Time Arrival (ETA)-based reward setting. Our contributions allow integrate in strategic phase. As consequence, can successfully avoid while greatly reducing occurrence secondary conflicts, as demonstrated numerical simulation results.