An Integrated Geometric Obstacle Avoidance and Genetic Algorithm TSP Model for UAV Path Planning

Abstract

In this paper, we propose an innovative approach for the path planning of Uninhabited Aerial Vehicles (UAVs) that combines an advanced Genetic Algorithm (GA) for optimising missions in advance and a geometrically based obstacle avoidance algorithm (QuickNav) for avoiding obstacles along the optimised path. The proposed approach addresses the key problem of determining an optimised trajectory for UAVs that covers multiple waypoints by enabling efficient obstacle avoidance, thus improving operational safety and efficiency. The study highlights the numerous challenges for UAV path planning by focusing on the importance of both global and local path planning approaches. To find the optimal routes, the GA utilises multiple methods of selection to optimise trajectories using the Cartesian Coordinate System (CCS) data transformed from a motion capture system. The QuickNav algorithm applies linear equations and geometric methods to detect obstacles, guaranteeing the safe navigation of UAVs and preventing real-time collisions. The proposed methodology has been proven useful in reducing the total distance travelled and computing times and successfully navigating UAVs across different scenarios with varying numbers of waypoints and obstacles, as demonstrated by simulations and real-world UAV flights. This comprehensive approach provides advantageous perspectives for real-world applications in a variety of operational situations and improves UAV autonomy, safety, and efficiency.

Keywords

• UAV path planning
• obstacle avoidance
• travelling salesmen problem
• genetic algorithm
• geometrical-based approach
• optimisation