Grey Wolf Optimization-Based Path Planning for Unmanned Aerial Vehicles in Bridge Inspection

This paper introduces a Grey Wolf Optimization (GWO) technique for addressing path planning challenges of unmanned aerial vehicles when inspecting an environment with multiple obstacles. By formulating a comprehensive cost function, the problem is cast as an optimization task that accounts for flight feasibility and safety constraints. The GWO algorithm is designed to efficiently explore the configuration space of UAVs, aiming to minimize the defined cost function and determine an optimal path. Simulations demonstrate the effectiveness and advantage of the proposed approach. Furthermore, the generated paths are validated through practical experiments, confirming their applicability for real UAV operations. This study underscores the potential of GWO for efficient and safe path planning in complex bridge inspection scenarios.