A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

To collect data of distributed sensors located at different areas in challenging scenarios through artificial way is obviously inefficient, due to the numerous labor and time. Unmanned Aerial Vehicle (UAV) emerges as a promising solution, which enables multi-UAV collect data automatically with the preassigned path. However, without a well-planned path, the required number and consumed energy of UAVs will increase dramatically. Thus, minimizing the required number and optimizing the path of UAVs, referred as multi-UAV path planning, are essential to achieve the efficient data collection. Therefore, some heuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) which works well for multi-UAV path planning have been proposed. Nevertheless, in challenging scenarios with high requirement for timeliness, the performance of convergence speed of above algorithms is imperfect, which will lead to an inefficient optimization process and delay the data collection. Deep learning (DL), once trained by enough datasets, has high solving speed without worries about convergence problems. Thus, in this paper, we propose an algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA), which combines the advantages of DL and GA. GA will collect states and paths from various scenarios and then use them to train the deep neural network so that while facing the familiar scenarios, it can rapidly give the optimized path, which can satisfy high timeliness requirements. Numerous experiments demonstrate that the solving speed of DL-GA is much faster than GA almost without loss of optimization capacity and even can outperform GA under some specific conditions.

Automated summary

Efficient data collection from distributed sensors in challenging scenarios can be achieved through well-planned multi-UAV path planning. The Deep Learning Trained by Genetic Algorithm (DL-GA) combines the advantages of DL and GA, providing rapid optimization of paths in high timeliness scenarios.