Collision avoidance mechanisms using artificial potential field for UAVs - Abdul Azis, Fadilah; Tan, Jie Sim; Md Ghazaly, Mariam; Mohamad Hanif, Noor Hazrin Hany (Research India Publications, 2025)
This paper presents an enhanced implementation of the Artificial Potential Field (APF) algorithm for collision avoidance in Unmanned Aerial Vehicles (UAVs), targeting the challenges of autonomous navigation in dynamic environments. The APF algorithm, based on the combination of attractive and repulsive potential functions, is modeled and simulated in MATLAB to guide UAVs toward target destinations while avoiding obstacles. To evaluate algorithmic performance, three simulated scenarios were developed: (i) varying obstacle densities, (ii) extreme goal positioning, and (iii) external wind disturbances. The accuracy of UAV trajectory tracking was quantitatively assessed using Root Mean Square Error (RMSE). Experimental results show that the APF algorithm generates collision-free paths with increasing RMSE values under more complex conditions: 0.05921 in obstacle-free space, 0.06349 with three obstacles, and 0.08666 under wind disturbance, indicating a 36.5% accuracy degradation due to environmental perturbations. The novelty of this study lies in integrating wind dynamics into the APF model and the systematic use of RMSE as a performance metric across multiple environmental conditions. The developed simulation framework serves as a reproducible platform for benchmarking and future algorithmic extensions. This research demonstrates the APF algorithm’s robustness and practical applicability while identifying limitations related to environmental adaptability and the need for parameter optimization in more complex flight conditions.