Abstract
We envision a future with companies providing civilian drone services to people - e.g., photo-taking on-demand for tourists from impossible (such as from off-the cliff) perspectives, object inspection, delivery, guarding, or helping someone check something out remotely. The broad aim of our work is to provide a framework for the cooperation between one or more stations and one or more drones as they are allocated to tasks. This paper has two contributions: (1) we propose a two-layered task servicing a model that couples the "big picture" across-drones perspective of drone stations, and the dynamic local perspective of drones, in order to decide which task a drone should serve next, where tasks are first allocated to a drone's task set via an on-station strategy, and then drones select tasks to serve from their respective task sets via an on-drone decision-making strategy, and (2) we report on our preliminary results on simulations to assess the impact of different station strategies (i.e., round-robin vs. serve-near), a selected drone strategy (i.e., utility function based), for different kinds of client distributions (i.e., random, scatter-near, scatter middle, and scatter-far), and for different numbers of drones. Our results show that the round-robin system performs better in most situations than serve-near for the allocation strategy.