Power-Aware Path Planning for Vehicle-Assisted Heterogeneous UAVs in Mobile Crowd Sensing

Abstract

With the development of microelectronics in recent years, the performance of unmanned aerial vehicles (UAVs) has been improving continuously. Modern rotary-wing UAVs possess high maneuverability and agility, making them widely applied in mobile crowd sensing (MCS). In order to solve the shortcoming of limited battery capacity and expand the mission area of UAVs, the ground vehicle is introduced as a platform for transportation, launch, recycle, and recharging UAVs. However, existing studies only consider the case of vehicle-assisted homogeneous UAVs. In reality, due to different sensing requirements and UAV hardware, vehicles may need to assist heterogeneous UAVs with different sensors, flight speeds, and battery capacities. In this paper, we formalize and study the vehicle-assisted heterogeneous UAVs path planning problem, and decompose it into three sub-problems, namely detection point allocation, UAV path planning, and vehicle route planning. In order to solve the above problems, we proposes an efficient power-aware path planning algorithm for vehicle-assisted multi-heterogeneous-UAV (VHUPA). In VHUPA, we first design the genetic algorithm to find the allocation scheme of the detection points, then plan flight paths of UAVs at each parking spot according to the allocation scheme, and finally optimize the route of the ground vehicle according to the power consumption of UAVs to minimize the waiting time for charging. Performance evaluation demonstrates that time cost of the VHUPA solution is reduced by more than 21% compared with the existing algorithm.