Improved Antlion Algorithm for UAV 3D Based on Multi-Strategy Path Planning

Path planning is a fundamental and practical function for Unmanned Aerial Vehicles (UAVs), especially when performing 3D operations in field environments. However, traditional path planning solutions have disadvantages, including local optimization, low convergence accuracy, and poor robustness. This paper proposes an improved Antlion algorithm (SLGCALO) to solve the above problems. We firstly construct flight trajectory model, field terrain model and constraint function for UAV kinematics. Based on this, an improved Sine chaotic mapping is used to generate task path points, while Levy flight is employed to update the UAV trajectories. Along with this, the Gauss-Cauchy hybrid mutation mechanism is introduced to avoid local optimization and improve convergence accuracy. Finally, simulation results are both presented, demonstrating that our proposed solution improves optimization accuracy and path quality compared to traditional algorithms such as the Ant Lion Optimizer (ALO).