Real-Time Performance Comparison of Vision-Based Autonomous Landing of Quadcopter on a Ground Moving Target

Abstract

The tremendous applications of unmanned aerial vehicles (UAVs), such as inspection in complex environments, search, and rescue missions, have established this area of research. The domain of UAVs autonomous navigation and landing has received considerable amount of interest of robotics researchers in the past decades. Nevertheless, the limited capability of UAVs for autonomous landing severely hampers the use of aerial vehicles specially in GPS-denied areas. Fortunately, with the rapid development of computer vision, vision-based techniques have become an important and cheap tool to be used in UAVs for moving target detection and landing. In this paper, four major vision-based techniques namely PID controller, fuzzy logic, sliding mode control and model predictive control have been investigated and their landing performance is compared to each other. These landing techniques are implemented on a quadcopter for the purpose of ground moving target detection and then landing on it. The Raspberry Pi board with two Pi cameras for 3D scene construction and depth estimation along with ultrasonic sensor have been used in the quadcopter. A wheeled mobile robot with landing platform is taken as the target. The landing performance of different techniques has been observed and compared in terms of landing displacement error, time taken and the distance travelled to finish the operation in an open and obstacle-free environment. The simulation results are further verified by the hardware experiments.