使用方向和速度自适应人工势场实现无人机在动态环境中的障碍物避障
Obstacle Avoidance of UAV in Dynamic Environments Using Direction and Velocity-Adaptive Artificial Potential Field
AI总结：
本文提出一种基于方向和速度自适应的人工势场方法，结合模型预测控制实现无人机在动态环境中的高效避障与安全导航。
摘要：
传统的人工势场（APF）本质上受到局部极小值问题和无法考虑移动障碍物运动学的限制。本文通过提出一种新的方向和相对速度加权人工势场（APF）来解决无人机在动态和拥挤空域中自主避障的关键挑战。在该方法中，引入了一个有界的加权函数$ω(θ,v_{e})$，根据障碍物相对于无人机的方向和速度动态调整排斥势。这种稳健的APF公式被整合到模型预测控制（MPC）框架中，以生成无碰撞轨迹并遵守运动学约束。仿真结果表明，所提出的方法有效解决了局部极小值问题，并显著提高了安全性，通过实现平滑且预测性的避障动作。系统确保了优越的路径完整性和可靠的性能，证实了其在复杂环境中的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
