提出多势场函数方法，提升无人机避障稳定性与响应速度。
A Multiple Artificial Potential Functions Approach for Collision Avoidance in UAV Systems
- 采用多个人工势场函数协同控制，避免单一势场缺陷。
- 推导参数调节条件，确保无人机最终位置稳定。
- 基于混合系统理论证明稳定性，适合实时避障场景。
碰撞避让是无人机应用中的关键挑战，受硬件限制、快速响应需求及障碍物探测不确定性影响。人工势场法（APF）虽被广泛使用，但现有方法难以保证闭环稳定，易引发抖动现象。为此，本文提出基于多人工势场函数（MAPOF）的静态障碍物避让控制方法。通过混合系统理论分析闭环系统，推导出确保最终位置稳定的控制参数调节条件。仿真结果验证了该方法在静态障碍物规避中的有效性，显著提升了避障过程的稳定性和可靠性。
原文摘要 · Abstract (English)
Collision avoidance is a problem largely studied in robotics, particularly in unmanned aerial vehicle (UAV) applications. Among the main challenges in this area are hardware limitations, the need for rapid response, and the uncertainty associated with obstacle detection. Artificial potential functions (APOFs) are a prominent method to address these challenges. However, existing solutions lack assurances regarding closed-loop stability and may result in chattering effects. Motivated by this, we propose a control method for static obstacle avoidance based on multiple artificial potential functions (MAPOFs). We derive tuning conditions on the control parameters that ensure the stability of the final position. The stability proof is established by analyzing the closed-loop system using tools from hybrid systems theory. Furthermore, we validate the performance of the MAPOF control through simulations, showcasing its effectiveness in avoiding static obstacles.
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