Nature-Inspired Algorithm Based Trajectory Planning for Inspection Flying Robot in Smart Grids

Developing new trends and technologies for power line inspection is critical for smart grid reliability. Due to the drawbacks of traditional power line methods, such as time consumption, high costs, and risks to worker’s safety, innovative technologies like flying robots need to be incorporated. Trajectory planning is crucial for optimizing path and conserving energy during flight, addressing challenges like collision avoidance, real-time planning, dynamic environments, and high-dimensional state spaces, for reliable motion of flying robots in inspection tasks. This study introduces a new trajectory planner for a flying robot, called quadrotor, designed for inspecting power lines within a smart grid infrastructure. The proposed approach utilizes the Water Cycle Algorithm (WCA) to find the most efficient trajectory within the 3D environment surrounding the power lines. The WCA algorithm emulates the water cycle’s dynamic processes, considering path length as an objective function while incorporating constraints such as collision avoidance, velocity limits, non-holonomic constraints, and execution time. The WCA's performance was evaluated against the Firefly Algorithm (FA) and the Particle Swarm Optimization (PSO), demonstrating superior path length minimization and enhancing efficiency for power line inspection in smart grids.