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.
คณะวิทยาศาสตร์
Developing a Smart Farming Simulation Utilizing LoRa Communication and Presenting Knowledge on LoRa Communication System Components
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
With the current cost of living situation in Thailand continuously rising, many recent graduates face challenges in managing their expenses in alignment with the increasing living costs. Food expenses, even for common street food, continue to surge with no sign of decreasing, despite improvements in raw material costs. Pay-Attention is a website platform designed to help recent graduates gain insights into managing and optimizing their food expenses effectively. It provides guidance on how to spend wisely, ensuring cost-effectiveness while maintaining adequate daily nutritional intake, without falling into monotonous eating habits.
คณะวิศวกรรมศาสตร์
During the recent years, PM2.5 concentration is rising above the safety exposure limit in Thailand. PM2.5 could have originated from various sources such as exhaust fumes, open air burning, wildfire, etc. This concludes that all cities or places would have different PM source contributions. Most studies regarding the PM source findings were done based on chemical analysis. Our research team would like to predict the PM sources physically by nanostructures analysis. These methods would require the PM dust to be collected in a limited amount of time and dry. The use of paper filters may cause contamination from filter material which may cause errors in result evaluation. Our team suggests using Electrostatic Precipitator (ESP) where electrostatics is used to capture PM dust. This research mainly focuses on designing and building the ESP system for PM collection whereas the requirement is to collect at least 100 mg of PM dust within 1 day which would be adequate for nanostructure analysis. The study revealed that the customized ESP system could achieve of up to 80% collecting efficiency (which is more than the commercial ESP that we previously used), there’s a also a parametric study of relationships between flow velocity and collecting efficiency where collecting efficiency is inversely proportional to flow velocity. The suggested air velocity is not to exceed 2 m/s. However, there’re still more room for improvement of the ESP system for PM collection such as the convenience of PM collection process which resulted from the ESP construction geometry and sizes.