This paper introduces a novel approach for quadrotor path planning referred to as the Fuzzy PID-inspired Adaptive Potential Field Method (FPID-APFM). Traditional Potential Field Methods (PFMs) suffer from well-known limitations, including vulnerability to local minima, oscillations near obstacles, and the need for manual fine-tuning of control parameters, which can significantly degrade performance in complex or dynamic environments. To overcome these issues, the proposed FPID-APFM incorporates a fuzzy inference system that adaptively regulates key parameters in real-time, specifically the attractive force coefficient (k_a) and the repulsive force coefficient (k_b). This parameter adjustment mechanism is inspired by PID control concepts, where k_a plays a role analogous to the proportional gain (k_p), directing the quadrotor toward its target, while k_b mimics the integral gain (k_i), considering cumulative interactions with nearby obstacles and contributing to more stable and responsive navigation. Additionally, a damping factor (ζ) is introduced to reduce oscillations and ensure smoother trajectory generation, particularly in dynamic and cluttered scenarios involving moving obstacles. The method’s effectiveness is validated through extensive simulation experiments conducted in both static and dynamic environments in MATLAB, including cases with wind disturbances and unpredictable changes. The results clearly demonstrate that FPID-APFM outperforms conventional approaches in terms of safety, smoothness, adaptability, and robustness. Overall, this method provides a reliable, flexible, and computationally efficient solution for real-world UAV navigation, package delivery, surveillance, search-and-rescue operations, and environmental monitoring.