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Generating Large-Scale Datasets for Spacecraft Pose Estimation via a High-Resolution Synthetic Image Renderer
(2025-04-01)
Hematulin, Warunyu
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Kamsing, Patcharin
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Phisannupawong, Thaweerath
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Panyalert, Thanayuth
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Manuthasna, Shariff
The trend toward conducting vision-based spacecraft pose estimation using deep neural networks, which necessitates accurately labeled datasets for training, is addressed in this paper. A method for generating an image regression-labeled dataset for spacecraft pose estimation through simulations involving Unreal Engine 5 is proposed herein. This work provides detailed algorithms for pose sampling and image generation, making it easy to reproduce the employed dataset. The dataset consists of images obtained under harsh lighting conditions and high-resolution backgrounds, featuring spacecraft models including Dragon, Soyuz, Tianzhou, and the ascent vehicle of Chang’E-6. The dataset comprises 40,000 high-resolution images, which are evenly distributed, with 10,000 images for each spacecraft model in scenes with both the Earth and the Moon. Each image is labeled with multivariate pose vectors that represent the relative position and attitude of the corresponding spacecraft with respect to the camera. This work emphasizes the critical role of realistic simulations in creating cost-effective synthetic datasets for training neural network-based pose estimators and publicly available for further study.
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Trajectory Planning for Multiple UAVs and Hierarchical Collision Avoidance Based on Nonlinear Kalman Filters
(2023-02-01)
Hematulin, Warunyu
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Kamsing, Patcharin
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Torteeka, Peerapong
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Somjit, Thanaporn
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Phisannupawong, Thaweerath
Fully autonomous trajectory planning for multiple unmanned aerial vehicles (UAVs) is significant for building the next generation of the logistics industry without human control. This paper presents a method to enable multiple UAVs to fly in the same trajectory without collision. It benefits several applications, such as smart cities and transfer goods, during the COVID-19 pandemic. Different types of nonlinear state estimation are deployed to test the position estimation of drones by treating the information from AirSim as offline dynamic data. The obtained global positioning system sensor data and magnetometer sensor data are determined as the measurement model. The experiment in the simulation is separated into (1) the localization state, (2) the rendezvous state, in which the proposed rendezvous strategy is presented by using the relation between velocity and displacement through the setting area, and (3) the full mission state, which combines both the localization and rendezvous states. The localization state results show the best RMSE in the case of full GPS available at 0.21477 m and 0.25842 m in the case of a GPS outage during a period of time by implementing the ensemble Kalman filter. Similarly, the ensemble Kalman filter performs well with an RMSE of 0.5112414 m in the rendezvous state and demonstrates exceptional performance in the full mission state. Moreover, the experiment is implemented in a real-world situation with some basic drone kits as proof that the proposed rendezvous strategy can truly operate.
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Cooperative Motion Planning for Multiple UAVs via the Bézier Curve Guided Line of Sight Techniques
(2022-01-01)
Hematulin, Warunyu
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Kamsing, Patcharin
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Torteeka, Peerapong
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Somjit, Thanaporn
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Phisannupawong, Thaweerath
Multiple motion planning plays an essential role in several vehicle motions. This paper proposes a cooperative method between the Line of Sight techniques and the Bézier curve, applying this to motion planning for unmanned aerial vehicles. The experiment is implemented using the AirSim plugin on Unreal Engine 4. The results of the proposed method are compared with those for the conventional Line of Sight techniques to control multiple unmanned aerial vehicles. The results illustrate that the proposed method takes more time to process than the conventional one. However, the proposed method can reach a higher performance by addressing the target unmanned aerial vehicles and the pre-defining path more than the conventional method does, which is shown in all three simulation cases.
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Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D∗algorithm
(2022-01-01)
Jarawan, Tanatthep
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Kamsing, Patcharin
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Torteeka, Peerapong
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Manuthasna, Shariff
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Hematulin, Warunyu
The indoor localization system is essential since the Global Positioning System cannot give an accurate position indoors, especially when several floor plans are considered. WiFi received signal strength becomes an alternative indicator for indoor localization systems. The experiment proposed a localization system created by integrating and working between the K-Nearest Neighbors algorithm and the D∗algorithm. The result illustrates the optimal path from the start point to the target point by avoiding the obstacle performing exceptionally well. The K-Nearest Neighbors algorithm provide the result for localization with Root Mean Square Errors of displacement at 1.190 meters, 2.491 meters, and 1.363 meters of X-Axis Y-Axis, respectively. The proposed indoor localization system can have various applications considering different environmental factors in different applications, such as the size of unmanned aerial vehicles when applying indoor unmanned aerial vehicles.
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Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D*algorithm
(2021-02-07)
Jarawan, Tanatthep
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Kamsing, Patcharin
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Tortceka, Peerapong
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Manuthasna, Shariff
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Hematulin, Warunyu
The indoor localization system is essential since the Global Positioning System cannot give an accurate position indoors, especially when several floor plans are considered. Wi-Fi received signal strength becomes an alternative indicator for indoor localization systems. The experiment proposed a localization system created by integrating and working between the K-Nearest Neighbors algorithm and the D*algorithm. The result illustrates the optimal path from the start point to the target point by avoiding the obstacle performing exceptionally well. The K-Nearest Neighbors algorithm provide the result for localization with Root Mean Square Errors of displacement at 1.190 meters, 2.491 meters, and 1.363 meters of X-Axis Y-Axis, respectively. The proposed indoor localization system can have various applications considering different environmental factors in different applications, such as the size of unmanned aerial vehicles when applying indoor unmanned aerial vehicles.
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Cooperative motion planning for multiple uavs via the bezier curve guided line of sight techniques
(2021-02-07)
Hematulin, Warunyu
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Kamsing, Patcharin
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Torteeka, Peerapong
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Somjit, Thanaporn
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Phisannupawong, Thaweerath
Multiple motion planning plays an essential role in several vehicle motions. This paper proposes a cooperative method between the Line of Sight techniques and the Bezier curve, applying this to motion planning for unmanned aerial vehicles. The experiment is implemented using the AirSim plugin on Unreal Engine 4. The results of the proposed method are compared with those for the conventional Line of Sight techniques to control multiple unmanned aerial vehicles. The results illustrate that the proposed method takes more time to process than the conventional one. However, the proposed method can reach a higher performance by addressing the target unmanned aerial vehicles and the pre-defining path more than the conventional method does, which is shown in all three simulation cases.
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Vision-based spacecraft pose estimation via a deep convolutional neural network for noncooperative docking operations
(2020-09-01)
Phisannupawong, Thaweerath
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Kamsing, Patcharin
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Torteeka, Peerapong
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Channumsin, Sittiporn
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Sawangwit, Utane
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.
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Aircraft trajectory recognition via statistical analysis clustering for Suvarnabhumi International Airport
(2020-02-01)
Kamsing, Patcharin
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Torteeka, Peerapong
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Yooyen, Soemsak
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Yenpiem, Siriporn
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Delahaye, Daniel
Since Suvarnabhumi International Airport is considered to be the biggest airport in Thailand, a big travelling-hub of southeast Asia and plays a significant part to the economy of Thailand relying on the tourism industry, an aircraft trajectory recognition is essential to support the high traffic management system from around the world. The first and essential stage of airport capacity enhancement is descriptive-analytic in several sections of the airport, including flight trajectory behaviors in order to plan an improvement procedure in the future. This experiment deploys K-mean and Gaussian Mixture clustering to compare results by using available automatic dependent surveillance-broadcast (ADS-B) dataset provided by the bigdata system from various websites. The test varies the number of clustering from three to ten and measures how similar an object is to its cluster by using the Silhouette score. Gaussian Mixture clustering produces at least three unique flight trajectories when setting the number of clustering equal to four, giving the Silhouette score of 0.43. K-mean clustering with the number of clustering equal to ten gives the highest Silhouette score of 0.45. However, its routes are not clearly recognized when compared with the Gaussian Mixture clustering. Although the overall results are not clearly shown in the pattern, it is enough to describe the trajectory patterns of the aircrafts taking off or landing over Suvarnabhumi International Airport.
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Vision-based attitude estimation for spacecraft docking operation through deep learning algorithm
(2020-02-01)
Phisannupawong, Thaweerath
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Kamsing, Patcharin
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Tortceka, Peerapong
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Yooyen, Soemsak
On-orbital services, especially in docking operation and other space object interaction. The missions need accurate, reliable, and robust detection to be an accurate in joining any interaction concerned. Two spacecrafts with an unknown mathematical model to predict the position and orientation, a computer vision-based attitude estimation system to detect the poses of spacecraft via camera is the key option of the mission. In astronautics control, the position coordinates are normally represented as the cartesian coordinate system and used a quaternions coordinate system for orientation representation because quaternions can represent the orientation of spacecraft better than physical angle and can overcome the problem of singularity. This paper aims to construct a model for both position and orientation estimation with public data. The input images are the dataset of Soyuz in the resolution of 1280x960, which is simulated by Unreal Engine 4. The implementation of this paper use GoogLeNet for a convolutional neural network model with the mathematical model of loss subject to direct regression. The result shows that a position estimation is significantly accurate with having distance error smaller than 1 meter and trand to reduce when setting a proper scaling factor for loss function. The result demonstrates a high error for orientation estimation. However, the experiment expresses that both position and orientation estimation can be improved in case of selecting a suitable scaling factor of loss function.

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