Study on A-Star Algorithm-Based 3D Path Optimization Method Considering Density of Obstacles

Collision avoidance and path planning are essential for ensuring safe and efficient UAV operations, particularly in applications like drone delivery and Advanced Air Mobility (AAM). This study introduces an improved algorithm for three-dimensional path planning in obstacle-rich environments, such as urban and industrial areas. The proposed approach integrates the A* search algorithm with a customized heuristic function which incorporates local obstacle density. This modification not only guides the search towards more efficient paths but also minimizes altitude variations and steers the UAV away from high-density obstacle regions. To achieve this, the A* algorithm was adapted to output obstacle density information at each path node, enabling a subsequent refinement process. The path refinement applies a truncation algorithm that considers both path angles and obstacle density, and the refined waypoints serve as control points for Non-Uniform Rational B-Splines (NURBS) interpolation. This process ensures smooth and dynamically feasible trajectories. Numerical simulations were performed using a quadrotor model with integrated PID controllers in environments with varying obstacle densities. The results demonstrate the algorithm's ability to effectively balance path efficiency and feasibility. Compared to traditional methods, the proposed approach exhibits superior performance in high-obstacle-density environments, validating its effectiveness and practical applicability.