The exponential growth of unmanned aerial vehicle (UAV) technology has spurred its adoption in diverse indoor applications, including infrastructure inspection, automated logistics, and emergency response. However, navigating through indoor environments, characterized by static obstacles, dynamic interferences, and spatial constraints, poses significant challenges to path planning algorithms. Developing efficient, robust, and real-time path planning solutions is crucial for enabling reliable autonomous UAV operations in such complex scenarios. This study presents a systematic approach to indoor UAV navigation, integrating custom hardware development, algorithmic innovation, and multi-faceted validation. An indoor UAV experimental platform was constructed around the Pixhawk 2.4.8 flight controller, complemented by a Firefly Core-3588L onboard computer, PXYZ-D435 depth camera, and OptiTrack motion capture system. After rigorous PID tuning and endurance testing, stable autonomous flight control was achieved via the Robot Operating System. Subsequent real-world tests on the custom UAV platform, involving obstacle courses and narrow passage traversals, further confirmed its robustness and stability in complex indoor environments. Overall, this research provides a practical framework for enhancing UAV navigation capabilities, with direct implications for real-world applications in logistics, surveillance, and emergency response.