Improved Artificial Potential Field Method for UAV Path Planning | Zendy

Qiang Han | Zendy; Xingyuan Ma | Zendy; Hanlin Liu | Zendy; Yibo Xu | Zendy; Yunxiang Xie | Zendy; Qianguo Yang | Zendy; Fanqin Meng | Zendy

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Improved Artificial Potential Field Method for UAV Path Planning

Author(s) -

Qiang Han,

Xingyuan Ma,

Hanlin Liu,

Yibo Xu,

Yunxiang Xie,

Qianguo Yang,

Fanqin Meng

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3620220

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Path planning for unmanned aerial vehicles (UAVs) is a core technology for autonomous navigation. The artificial potential field (APF) method, known for its computational efficiency and strong real-time performance, has been widely applied. However, the traditional approach suffers from three inherent defects: target unreachability, local minima, and local path oscillation. To address these issues, this paper proposes an Improved Artificial Potential Field (IAPF) method, innovatively incorporating three types of optimization mechanisms: 1) A target point distance-weighted function to reconstruct the repulsive field, ensuring terminal convergence; 2) A local exploration factor to achieve escape from local minima; 3) A directional weighting factor and a forward-looking decision-making mechanism to suppress path oscillations. Moreover, experiments have demonstrated the ability of the proposed algorithm to address the issues in traditional APF, as well as its robustness and superiority in complex three-dimensional environments.

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