Real Time Airborne Collision Avoidance Using Multiple Quadcopters

Abstract

Our project deals with the real time collision avoidance between two UAV’s in real time. The solution for this problem is divided into five parts. The first and foremost part is controlling the path of drone using either ROS (Robotic Operating System) or Python. The next step is detection of nearby UAV. For this purpose we have used onboard sensor (camera) and deep learning methodologies. The third step is state estimation in which we find the position and distance of detected drones to find out where the drone is present. We have used Sonar sensors to measure the distance of nearby UAV. The fourth step is to estimate the chance of collision and finally if the collision is detected we need some collision avoidance mechanism. Collision between two objects when either one of the object is static is a relatively simple problem and has already been dealt with, but our problem involves collision between two moving objects which is a far complicated problem and involves various factors. In this report, we’ll mention what those factors are and how we are able to deal with some of them. We have used multiple approaches in each step. For example state estimation is also done by using sensors and computer vision based approach. We have listed these multiple approaches and also the challenges we faced in each approach and why we have selected one particular approach. We also present a data augmentation technique we applied for generating the dataset, which is then used for drone detection and also made this dataset available to use for public.The technique is quite simple and generic and can be used for generating any type of such simple datasets. Results and conclusions also mention the challenges we faced in the implementation of this project and how the stability of UAV is a major factor in making the UAV autonomous and deploying collision avoidance on actual UAV.