Comparison of A* and D* Algorithms for 3D Path Planning of Unmanned Aerial Vehicles

Finding an optimal and collision-free path from a source node to a target one in the presence of environmental obstacles is a challenging task, especially in missions involving Unmanned Aerial Vehicles (UAVs). These robots navigate through 3D space, following aerial waypoints to reach their destinations, adding complexity to the path planning problem. In this paper, a comparative study of two of the most popular geometric model search path planning approaches, i.e. A* and D* algorithms, is conducted in various navigation scenarios with progressive complexity and increased numbers of static obstacles. Demonstrative results in terms of path length, computational time and collision avoidance capability are performed over complex scenarios. By comparing the performance metrics of these algorithms, valuable insights can be gained regarding their efficiency and effectiveness in finding optimal paths for UAVs. This research study aims to provide a comprehensive understanding of the strengths and limitations of each algorithm, aiding in informed decision-making for path planning in 3D environments involving UAVs.