Abstract
<jats:p>Unmanned Aerial Vehicles (UAVs) are revolutionizing power grid inspection, yet ensuring reliable, high-quality image transmission in complex environments remains a significant challenge for autonomous operations. Traditional path planning algorithms often neglect communication constraints, risking mission failure in areas with poor connectivity—a critical concern for future UAV swarm deployments where inter-agent coordination depends on sustained link quality. This paper proposes a novel communication-aware path planning framework to address this gap. We formulate the problem as a multi-objective optimization that simultaneously considers safety, complete inspection coverage, path length, and communication quality. The core of our solution is an Improved Grey Wolf Optimizer (IGWO), which incorporates a nonlinear convergence factor, a genetic mutation strategy, and a modified leader update mechanism to enhance global search capability and avoid local optima. To evaluate the proposed framework, we adopt a dual-scenario validation strategy: in the mountainous setting, we employ a synthetic conical-peak terrain model with the standardized 3rd Generation Partnership Project (3GPP) Rural Macrocell (RMa) channel model; in the urban setting, we use real-world building footprints from OpenStreetMap (Hangzhou, China) with deterministic ray-tracing propagation modeling. Comprehensive simulations in both scenarios demonstrate that IGWO achieves a 50% success rate in dense urban areas, significantly outperforming Particle Swarm Optimization (PSO) at 10% and Genetic Algorithm (GA) at 28%. Crucially, communication-aware paths reduce average outage duration by over 65% while often yielding shorter paths. This work confirms that explicit communication modeling is indispensable for reliable UAV inspection and that the proposed IGWO offers a robust and scalable solution for future autonomous and cooperative power grid maintenance systems.</jats:p>