In this study, we focus on the path-planning problem of unmanned aerial vehicles (UAVs) deployed for inspection missions at target points. The goal is to visit each target point, provide revisits to important target p...
详细信息
In this study, we focus on the path-planning problem of unmanned aerial vehicles (UAVs) deployed for inspection missions at target points. The goal is to visit each target point, provide revisits to important target points, and ultimately meet the monitoring requirements with regular and stable monitoring frequencies. Herein, we present MTSP-R, a novel variant of the multiple traveling salesmen problem (MTSP), in which revisits to important target points are allowed. We address the path- planning problem of multi-UAV in two stages. First, we propose a nearest insertion algorithm with revisits (NIA-R) to determine the number of required UAVs and initial inspection paths. We then propose an improved genetic algorithm (IGA) with two-part chromosome encoding to further optimize the inspection paths of the UAVs. The simulation results demonstrate that the IGA can effectively overcome the shortcomings of the original genetic algorithm, providing shorter paths for multiple UAVs and more stable monitoring frequencies for the target points.
The deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) is gaining recently more research interest, thanks to the numerous advantages of UAVs. In fact, UAVs can be depl...
详细信息
ISBN:
(数字)9781665482431
ISBN:
(纸本)9781665482431
The deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) is gaining recently more research interest, thanks to the numerous advantages of UAVs. In fact, UAVs can be deployed quickly almost anywhere and are able to access difficult terrains. However, both WSNs and UAV suffer from serious energy limitation challenges. Fortunately, when they are available, using multiple UAVs along with appropriate trajectory planning schemes can highly reduce the negative impact of this problem. In this paper, we deal with the problem of data collection in a WSN assisted by multiple UAVs. Our main goal is to minimize the mission total time by jointly optimizing the trajectories of all the UAVs while serving the sensor nodes (SNs). The mission total time is defined as the time required by the UAVs to transfer energy to all using wireless power transfer (WPT) and collect data from these SNs. In addition to the highly complex optimal solution, we propose two heuristics to solve the formulated problem, namely the nearest insertion algorithm (NIA) and the greedy algorithm (GA).
暂无评论