Research on Path Planning for Uav Reconnaissance in Mountainous Terrain

To achieve path planning for Unmanned Aerial Vehicle (UAV) reconnaissance in mountainous environments, an environmental model was constructed based on a real-world mountainous setting. A fitness function was designed incorporating the UAV path length cost and flight altitude cost. For the obstacle avoidance mechanism, a path repair function with a safety margin was proposed, and a path simplification strategy was employed to reduce computational complexity. To address the shortcomings of the traditional Harris Hawks Optimization (HHO) algorithm, performance was enhanced through the following four improvements: (1) Utilizing piecewise chaotic mapping to increase population diversity; (2) Introducing a nonlinear convergence factor to balance global exploration and local exploitation; (3) Designing an adaptive <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{t}$</tex>-distribution mutation strategy to avoid local optima; (4) Constructing nonlinear control parameters to improve convergence speed. In the experiment, the results of the fitness function and whether to achieve obstacle avoidance are comprehensively considered. Experimental results demonstrate that this method effectively satisfies the requirements for UAV reconnaissance path planning in mountainous environments, performing well overall.