Trajectory Planning for UAVs in Static Environments with Circular Obstacles

The UAV (Unmanned Aerial Vehicle) path planning problem is inherently complex due to the need to optimize multiple conflicting objectives under dynamic and uncertain conditions. It involves finding a collision-free, energy-efficient, and time-optimal path from a start point to a destination while considering obstacles, no-fly zones, limited battery capacity, and real-time environmental changes such as wind or moving objects. The present work focuses on solving the problem of trajectory planning in UAVs in static environments containing circular radar-like obstacles. The objective is to find a collision-free, optimal path to move the UAV from an initial position to a specified target point, avoiding static obstacles. As a solution to this problem, we utilize Particle Swarm Optimization (PSO), a metaheuristic technique derived from observations of group behavior in animals like birds, fish, etc. The approach adopted here leverages both the exploitation and exploration aspects of PSO in a pragmatic way to scan through the problem space to find optimal, or near-optimal, trajectories. By incorporating obstacle constraints into a fitness function, its suggested methodology ensures both efficiency as well as safety in route planning. The simulation results validate the efficiency, robustness, and effectiveness of the suggested methodology in navigating through challenging static environments with high success rates at relatively low computational costs.