A-star path planning simulation for UAS Traffic Management (UTM) application
This work addresses drone traffic management for urban applications, but it is incremental as it applies existing algorithms to a new simulation setup.
The paper tackled drone path planning in urban environments by implementing and comparing 2D and 3D A-star algorithms with different heuristics, finding that Euclidean distance increased execution time by 3.2x and 17.2x for 2D and 3D versions, respectively.
This paper presents a Robot Operating System and Gazebo application to calculate and simulate an optimal route for a drone in an urban environment by developing new ROS packages and executing them along with open-source tools. Firstly, the current regulations about UAS are presented to guide the building of the simulated environment, and multiple path planning algorithms are reviewed to guide the search method selection. After selecting the A-star algorithm, both the 2D and 3D versions of them were implemented in this paper, with both Manhattan and Euclidean distances heuristics. The performance of these algorithms was evaluated considering the distance to be covered by the drone and the execution time of the route planning method, aiming to support algorithm's choice based on the environment in which it will be applied. The algorithm execution time was 3.2 and 17.2 higher when using the Euclidean distance for the 2D and 3D A-star algorithm, respectively. Along with the performance analysis of the algorithm, this paper is also the first step for building a complete UAS Traffic Management (UTM) system simulation using ROS and Gazebo.