UAV Landing Platform Recognition Using Cognitive Computation Combining Geometric Analysis and Computer Vision Techniques

Unmanned aerial vehicles (UAVs) are excellent tools with extensive demand. During the last phase of landing, they require additional support to that of GPS. This can be achieved through the UAV's perception system based on its on-board camera and intelligence, and with which decisions can be made as to how to land on a platform (target). A cognitive computation approach is proposed to recognize this target that has been specifically designed to translate human reasoning into computational procedures by computing two probabilities of detection which are combined considering the fuzzy set theory for proper decision-making. The platform design is based on: (1) spectral information in the visible range which are uncommon colors in the UAV's operating environments (indoors and outdoors) and (2) specific figures in the foreground, which allow partial perception of each figure. We exploit color image properties from specific-colored figures embedded on the platform and which are identified by applying image processing and pattern recognition techniques, including Euclidean Distance Smart Geometric Analysis, to identify the platform in a very efficient and reliable manner. The test strategy uses 800 images captured with a smartphone onboard a quad-rotor UAV. The results verify the proposed method outperforms existing strategies, especially those that do not use color information. Platform recognition is also possible even with only a partial view of the target, due to image capture under adverse conditions. This demonstrates the effectiveness and robustness of the proposed cognitive computing-based perception system.

Automated summary

Unmanned aerial vehicles (UAVs) require additional support during the last phase of landing, for which a cognitive computing-based perception system is proposed in this study. This system utilizes on-board camera and intelligence to recognize the specially designed target, allowing the UAVs to land on the platform. The proposed method outperforms existing strategies, especially in the use of color information, as demonstrated in the test with 800 images captured by a smartphone onboard a quad-rotor UAV.