An Energy-Balanced Path Planning Algorithm for Multiple Ferrying UAVs Based on GA

Abstract

When performing a search and rescue mission, unmanned aerial vehicles (UAVs) should continuously search targets above the mission area. In order to transfer the search and rescue information quickly and efficiently, two types of UAVs, the ferrying UAVs and the searching UAVs, are used to complete the mission. Obviously, this application scenario requires an efficient path planning method for ferrying UAVs. The existing path planning methods for ferrying UAVs usually focus on shortening the path length and ignore the different initial energy of ferrying UAVs. However, the following problem does exist: if the ferrying UAV with less initial energy is assigned a longer path, meaning that the ferrying UAV with less initial energy will ferry messages for more searching UAVs. When the lower-initial-energy ferrying UAV is running out of energy, more searching UAVs will no longer deliver messages successfully. Therefore, the mismatch between the planned path length and the initial energy will eventually result in a lower global message delivery ratio. To solve this problem, we propose a new concept energy-factor for a ferrying UAV and use the variance of all ferrying UAVs’ energy-factor to measure the balance between the planned path length and the initial energy. Further, we model the energy-balanced path-planning problem for multiple ferrying UAVs, which actually is a multiobject optimization problem of minimizing the planned path length and minimizing the variance of all ferrying UAVs’ energy-factor. Based on the genetic algorithm, we design and implement an energy-balanced path planning algorithm (EMTSPA) for multiple ferrying UAVs to solve this multiobject optimization problem. Experimental results show that EMTSPA effectively increases the global message delivery ratio and decreases the global message delay.

UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

“…Metaheuristic optimization methods are another path planning algorithms. Among them, simulated annealing [18], ant colony [6], genetic algorithm [19][20][21][22][23][24], and PSO [3,20,22,25,26] algorithms have been widely used for path planning. In terms of implementing dynamic constraints of UAV, GA and PSO algorithms are preferable to the graph-based algorithms.…”