Evaluation of Reactive Collision Avoidance Algorithms for Unmanned Aerial Vehicles

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) platform 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 two 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 two algorithms tested in this paper are based on artificial potential fields and inverse proportional navigation. Artificial potential fields maintain strong performance across all categories because of the algorithm’s handling of many special cases. Inverse proportional navigation outperformed artificial potential fields by handling all scenarios with perfection. Furthermore, artificial potential fields failed to handle all of the stressful simulations, especially when the airspace became congested, while inverse proportional navigation handled all scenarios. While artificial potential fields is able to handle up to sixteen aircraft on a 500 meter square field and thirty-two aircraft safely on a 1000 meter square field, inverse proportional navigation is able to handle up to thirty-two aircraft on both a 500 meter square field and a 1000 meter square field.