AI Empowered Intelligent Search for Path Planning in UAV-Assisted Data Collection Networks

Unmanned aerial vehicle (UAV) assisted data collection has been extensively employed in various application scenarios, e.g., non-terrestrial networks for disaster management, agricultural crop protection, environmental monitoring. However, data collection and transmission model in different applications are not universal, and the timeliness of large-scale data collection and transmission also has been remained as a challenge. To address this issue, Artificial Intelligence (AI) empowered intelligent search algorithms for path planning in UAV-assisted data collection networks are investigated in this paper. With the constraints including energy consumption, transmission distances and full coverage of sensors, a data collection model using UAV in hovering mode is firstly established for minimizing the flight distances of UAVs, and an adaptive full coverage algorithm (AFCA) is proposed to optimize the quality of service through using the model. Subsequently, for optimizing the path planning of UAVs, an intelligent path planning algorithm (IPPA) is proposed through considering the loop and non-crossing characteristics presented by the optimal paths. In six testing cases with different sensor sizes, the experimental results have been shown to demonstrate that the proposed solution outperforms the traditional algorithms.