The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
In one, docking is defined as “when one incoming spacecraft rendezvous with another spacecraft and flies a controlled collision trajectory in such a manner to align and mesh the interface mechanisms”, and defined docking as an on-orbital service to connect two free-flying man-made space objects. The service should be supported by an accurate, reliable, and robust positioning and orientation (pose) estimation system. Therefore, pose estimation is an essential process in an on-orbit spacecraft docking operation. The position estimation can be obtained by the most well-known cooperative measurement, a Global Positioning System (GPS), while the spacecraft attitude can be measured by an installed Inertial Measurement Unit (IMU). However, these methods are not applicable to non-cooperative targets. Many studies and missions have been performed by focusing on mutually cooperative satellites. However, the demand for non-cooperative satellites may increase in the future. Therefore, determining the attitude of non-cooperative spacecrafts is a challenging technological research problem that can improve spacecraft docking operations. One traditional method, which is based on spacecraft control principles, is to estimate the position and attitude of a spacecraft using the equations of motion, which are a function of time. However, the prediction using a spacecraft equation of motion needs support from the sensor fusion to achieve the highest accuracy of the state estimation algorithm. For non-cooperative spacecraft, a vision-based pose estimator is currently developing for space application with a faster and more powerful computational resource.
คณะเทคโนโลยีการเกษตร
The research aims to develop chili Thai commercial varieties for resistance to anthracnose and Pepper yellow leaf curl virus disease. The varieties allowing farmer to reduce the use of chemical pesticides for disease and pest control, also increases productivity and lowers production costs for farmers. The development new varieties are under studied of undergraduate, master's, and doctoral students by using conventional and molecular plant breeding. The new chili varieties were released to farmer and commercial companies for development for Thai commercial seed industry.
คณะเทคโนโลยีสารสนเทศ
The project "30 Days Harvest" is inspired by the significant role agriculture plays in human society, particularly in providing food for consumption. However, farmers often face numerous challenges arising from environmental issues. The developers chose to highlight these problems through the creation of 30 Days Harvest, aiming to raise awareness among players about the impacts that farmers must endure, while still ensuring the game is enjoyable. The game also offers players the opportunity to think critically and devise strategies to deal with various challenges on their own. When the game starts, players take on the role of a farmer who must manage their farm to harvest the highest yield possible. The produce can be used to prepare meals or sold to customers within a limited timeframe. The goal of the game focuses on maximizing profits, which requires careful planning and analysis to overcome environmental obstacles. Additionally, players must ensure that their agricultural production is sufficient for food preparation. Along the way, they will also learn about the environmental impacts affecting agriculture.
คณะวิทยาศาสตร์
Metallic nanoparticles embedded in cellulose nanocrystal (MNPs/CNC) films were prepared by solution casting for antimicrobial and fungus in edible peel fruit. MNPs/CNC was synthesized by ultrasonic waves. The as-synthesized was characterized by the chemical characteristics by the transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction analysis (XRD), Fourier transform infrared spectrometer (FT-IR), zeta potential analyzer, and UV-visible spectrophotometer. MNPs/CNC films had high potential in antimicrobial and fungus. Therefore, MNPs/CNC can be used to wrap edible peeled fruit to inhibit the growth of microorganisms, which can effectively extend the shelf life of fruits.