Comparison of Aerial Collision Avoidance Algorithms in a Simulated Environment

Abstract

In the field of unmanned aerial vehicles (UAVs), several control processes must be active to maintain safe, autonomous flight. When flying multiple UAVs simultaneously, these aircraft must be capable of performing mission tasks while maintaining a safe distance from each other and obstacles in the air. Despite numerous proposed collision avoidance algorithms, there is little research comparing these algorithms in a single environment. This paper outlines a system built on the Robot Operating System (ROS) environment that allows for control of autonomous aircraft from a base station. This base station allows a researcher to test different collision avoidance algorithms in both the real world and simulated environments. Data is then gathered from three prominent collision avoidance algorithms based on safety and efficiency metrics. These simulations use different configurations based on airspace size and number of UAVs present at the start of the test. The three algorithms tested in this paper are based on mixed integer linear programming (MILP), the A* algorithm, and artificial potential fields. The results show that MILP excelled with a small number of aircraft on the field, but has computation issues with a large number of aircraft. The A* algorithm struggled with small field sizes but performed very well with a larger airspace. Artificial potential fields maintained strong performance across all categories because of the algorithm’s handling of many special cases. While no algorithms were perfect, these algorithms demonstrated the ability to handle up to eight aircraft on a 500 meter square field and sixteen aircraft safely on a 1000 meter square field.