Landing System For Unmanned Aerial Vehicles Research Articles

Vision algorithms for fixed-wing unmanned aerial vehicle landing system

Autonomous landing has become a core technology of unmanned aerial vehicle (UAV) guidance, navigation and control system in recent years. As a novel autonomous landing approach, computer vision has been studied and applied in rotary-wing UAV landing successfully. This paper aims to fixed-wing UAV and focus on two problems: how to find runway only depending on airborne front-looking camera and how to align UAV with the designated landing runway. The paper can be divided into two parts to solve above two problems respectively. In the first part, the paper firstly presents an algorithm of region of interest (ROI) detection, which is based on spectral residual saliency map, and then an algorithm of feature vector extraction based on sparse coding and spatial pyramid matching (SPM) is proposed, finally, ROI including designated landing runway is recognized by a linear support vector machine. In the second part, the paper presents an approach of relative position and pose estimation between UAV and landing runway. Estimation algorithm firstly selects five feature points on the runway surface, and then establishes a new earth-fixed reference frame, finally uses orthogonal iteration to estimate landing parameters including three parameters of distance, height and offset, and three pose parameters of roll, yaw, pitch. The experimental results verify the effectiveness of the algorithms proposed in this paper.

Improved Vision-Based Algorithm for Unmanned Aerial Vehicles Autonomous Landing

In vision-based autonomous landing system of UAV (Unmanned Aerial Vehicle), the efficiency of object detection and tracking will directly affect the control system. An improved algorithm of SURF (Speed Up Robust Features) will resolve the problem which is inefficiency of the SURF algorithm in the autonomous landing system of UAV. The improved algorithm is composed of three steps: first, detect the region of the target using the Camshift (Continuously Adaptive Mean-SHIFT) algorithm; second, detect feature points in the region of the above acquired using the SURF algorithm; third, do the matching between the template target and the region of target in frame. Results of experiments and theoretical analysis testify the efficiency of the algorithm. We also show the experiments that demonstrate that the UAV can autonomous land without using GPS, or without specific assumptions about the environment.

UAV landing system simulation model software system

A software system for a simulation model of a UAV's automatic landing system is presented herein. The simulation model allows us to determine the level of influence of different distortions of the observed image on the UAV's landing accuracy. The correlation extremal system of terminal guidance (CESTG) of an unmanned aerial vehicle (UAV) executed on the basis of an optoelectronic sensor has found its application as a tool providing the terminal delivery of UAV's freight to the planned Earth surface area or automatic landing of an unmanned helicopter (UH) on a selected place or on a surface ship deck. In operation, the CESTG carries out a search for a landing place (freight delivery), guidance to the selected landing place (freight delivery) at the UAV's approach and the exact path of the UAV to the selected area in the processing of current information receiving at the discrete moments of time (shot shaping time) about the surveillance area from the optoelectronic sensor.