Autonomous UAV Path Planning Using Modified PSO for UAV-Assisted Wireless Networks

Recently, unmanned aerial vehicles (UAVs) have attained considerable attention for providing reliable and cost-effective communication due to the flexibility of deployment and line of sight (LoS) propagation. Efficient UAV path planning is one of the key aspects that need to be addressed to minimize energy consumption and satisfy the rate requirements of the user. Thus, in this work, we propose a novel framework that utilizes the modified Particle Swarm Optimization (PSO) algorithm for UAV path planning to support the rate requirements of the user. In the proposed framework, the problem of joint path planning and energy consumption is formulated to improve the instantaneous sum rate of the user. In order to solve the formulation, the proposed framework involves two steps. Initially, the line of sight probability is used to obtain an optimal destination location at which the UAV is in LoS with the user and offers the required downlink rate. Following that, the modified PSO is used to find the most energy-efficient path from the source to the destination. Through experiments, we show that the proposed framework provides a three-dimensional (3D) path in a complex environment, and has the ability to avoid obstacles in the path. In addition, it minimizes energy consumption and travel time and improves the user rate as compared to the state-of-the-art methods. Finally, the performance of the proposed framework is tested in three different scenarios and shows that the proposed method performs better than the state-of-the-art methods in all scenarios.