Heuristic Path‐Planning Techniques in Indoor Complex Three Dimensional Environment for Unmanned Aerial Vehicles

ABSTRACT Unmanned Aerial Vehicles (UAVs) are ubiquitous in diverse applications, underscoring the need for efficient path‐planning, particularly in complex three‐dimensional (3D) environments. Current heuristic path‐planning algorithms are largely designed for two‐dimensional (2D) contexts, with a select few adapted for 3D open spaces. The application of these algorithms in 3D indoor or maze environments, however, remains largely unexplored. To address this gap, this study implements Dijkstra's, Greedy BFS, A*, Beam Search A*, Iterative Deepening A* (IDA*), Theta*, Weighted A* (WA*), Dynamic Weighted A* (DWA*), D* in 3D‐environment and presents a comparative analysis within 3D indoor and maze scenarios. We assess their performance based on parameters such as path length, computational time, the number of points needed to reach the goal, and notably, memory consumption‐a key consideration in UAVs due to their limited onboard memory. Through this analysis, we provide crucial insights into the behavior of these algorithms in complex 3D environments, thus informing the selection and development of optimal path‐planning strategies for future UAV applications.