Autonomous UAV Navigation in Simulated Environments: A Comparative Study of Dijkstra and A* Algorithms

This study presents a comparative analysis of the Dijkstra and A* algorithms for the autonomous path planning of Unmanned Aerial Vehicles (UAVs) in simulated 2D environments. The simulations were conducted in CoppeliaSim (V-REP), a versatile robotics simulation platform, where a quadcopter model navigated through obstacle-rich scenarios by following the shortest path generated by each algorithm. Both algorithms were implemented using a grid-based graph representation, with the path costs calculated using the Manhattan and Euclidean distances. The UAV visually traced the computed path in real time, avoided obstacles, and returned to the starting point after reaching the target. Performance metrics such as path optimality, computational efficiency, and execution time were evaluated to compare the two approaches. The results indicate that while Dijkstra guarantees the shortest path, A* achieves faster convergence with minimal deviation in path length, making it more suitable for real-time UAV navigation. The visualized simulation framework demonstrates the effectiveness of integrating classical pathfinding algorithms with UAV models in a physics-enabled environment, offering a reproducible testbed for autonomous navigation research.