A Path Planning Method for UAV Target Detection Based on Improved Genetic Algorithm

The studies of Unmanned aerial vehicles (UAVs) have received much attention due to their wide applications in the courier delivery, target detection, precision agriculture, and disaster rescue and others. Path planning is one of the core technologies in the UAV field, aiming to provide feasible, safe, and optimal flight paths for UAVs to meet the flying requirements in complex environments. Effective path planning can improve the adaptability of UAVs in various complex environments. Traditional path planning methods, although capable of finding reasonable paths, often suffer from slow convergence speed and the tendency to fall into local optima. To address these shortcomings, this paper proposes an improved Genetic Algorithm (MLGA) combining Meta-learning and local search mechanisms for UAV target detection task path planning. The algorithm can dynamically adjust parameters based on the performance at different stages, allowing it to self-optimize according to task changes, while enhancing its local search capability. This leads to improved solution quality and faster convergence. Simulation results show that MLGA outperforms several other algorithms in solving path planning problems.