Enhancing Indoor Path Planning for UAV through Hybrid Artificial Intelligent Approach

In mobile robot navigation, path planning of unmanned aerial vehicles (UAV) remains an important problem, particularly for indoor environments. Indoor environments consist of various challenges such as limited GPS signal, presence of static and dynamic obstacles, narrow passages, low flight height, stability, and maneuverability etc. Due to those issues, it is very difficult to navigate in such an environment with precise control and safe navigation. Hence, there is a need of robust and adaptable artificial intelligent path planners to address these challenges. This paper addresses the application of A* Algorithm and Fuzzy Logic as a hybrid approach for path planning of UAVs for indoor navigation. In a static context, the study shows the simulation and experimental validation. The results demonstrate how well the hybrid A* Algorithm and Fuzzy Logic work together to accomplish path planning objectives like navigational time and optimal path design. Less than $6 \%$ separates the simulated and experimental results as a percentage. Additionally, the effectiveness of the suggested hybrid technique is evaluated against standalone algorithms like A* and Fuzzy Logic, with the suggested hybrid approach yielding $7.5 \%$ shorter paths and less navigation time.