Fair-Energy Trajectory Planning for Cooperative UAVs to Locate Multiple Targets

Abstract

Due to the flexibility and affordability, multi-target positioning based on cooperation of Unmanned Aerial Vehicles (UAVs) becomes attractive in recent years. Trilateration is popular and easy to implement, but still faces challenges in multi-UAV scenario. First, multiple distance measurements from a single UAV on same targets will lead to large accumulated errors. Second, the time interval between successive distance measurements on the same target cannot be long due to the mobility of the target. Finally, UAVs have limited onboard energy which constrains the flight duration and the mission will fail when some UAVs reach the limitation. In this paper, to complete multi-target positioning mission, we aim at minimizing the maximum energy consumption among all UAVs, which can be decomposed into two subproblems after dividing all UAVs into groups of three. Then we propose a two-stage heuristic algorithm, in which we first use adjusted Genetic Algorithm (GA) to plan the trajectories of all groups with bounded maximum energy consumption and then we pursue to minimize the maximum energy consumption among UAVs in a group. Compared to two other algorithms, extensive simulations show that the proposed algorithm can reduce up to 24.9% and 11.8% in terms of maximum and average energy consumption, respectively.