The conventional Artificial Potential Field (APF) is fundamentally limited by the local minima issue and its inability to account for the kinematics of moving obstacles. This paper addresses the critical challenge of autonomous collision avoidance for Unmanned Aerial Vehicles (UAVs) operating in dynamic and cluttered airspace by proposing a novel Direction and Relative Velocity Weighted Artificial Potential Field (APF). In this approach, a bounded weighting function, $ω(θ,v_{e})$, is introduced to dynamically scale the repulsive potential based on the direction and velocity of the obstacle relative to the UAV. This robust APF formulation is integrated within a Model Predictive Control (MPC) framework to generate collision-free trajectories while adhering to kinematic constraints. Simulation results demonstrate that the proposed method effectively resolves local minima and significantly enhances safety by enabling smooth, predictive avoidance maneuvers. The system ensures superior path integrity and reliable performance, confirming its viability for autonomous navigation in complex environments.

翻译：传统人工势场（APF）方法存在局部极小值问题，且无法有效处理运动障碍物的运动学特性。本文针对无人机在动态复杂空域中自主避障的关键挑战，提出一种新颖的方向与相对速度加权人工势场（APF）。该方法引入有界加权函数 $ω(θ,v_{e})$，根据障碍物相对于无人机的方向与速度动态调节排斥势场的强度。该鲁棒APF模型被集成至模型预测控制（MPC）框架中，在满足运动学约束的同时生成无碰撞轨迹。仿真结果表明，所提方法有效解决了局部极小值问题，并通过实现平滑、预测性的避障机动显著提升了安全性。该系统保证了优异的路径完整性与可靠性能，验证了其在复杂环境中自主导航的可行性。