| 1. |
- Alepuz, Javier Pérez, et al.
(author)
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Direct image-based visual servoing of free-floating space manipulators
- 2016
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In: Aerospace Science and Technology. - : Elsevier BV. - 1270-9638 .- 1626-3219. ; 55, s. 1-9
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Journal article (peer-reviewed)abstract
- This paper presents an image-based controller to perform the guidance of a free-floating robot manipulator. The manipulator has an eye-in-hand camera system, and is attached to a base satellite. The base is completely free and floating in space with no attitude control, and thus, freely reacting to the movements of the robot manipulator attached to it. The proposed image-based approach uses the system's kinematics and dynamics model, not only to achieve a desired location with respect to an observed object in space, but also to follow a desired trajectory with respect to the object. To do this, the paper presents an optimal control approach to guiding the free-floating satellite-mounted robot, using visual information and considering the optimization of the motor commands with respect to a specified metric along with chaos compensation. The proposed controller is applied to the visual control of a four-degree-of-freedom robot manipulator in different scenarios.
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| 2. |
- Allesøe, Rosa Lundbye, et al.
(author)
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Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
- 2023
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In: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 41:3, s. 399-408
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Journal article (peer-reviewed)abstract
- The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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| 3. |
- Pomares, Jorge, et al.
(author)
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Concurrent Image-based Visual Servoing with Adaptive Zooming for Non-cooperative Rendezvous Maneuvers
- 2018
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In: Advances in Space Research. - : Elsevier. - 0273-1177 .- 1879-1948. ; 61:3, s. 862-878
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Journal article (peer-reviewed)abstract
- An image-based servo controller for the guidance of a spacecraft during non-cooperative rendezvous is presented in this paper. The controller directly utilizes the visual features from image frames of a target spacecraft for computing both attitude and orbital maneuvers concurrently. The utilization of adaptive optics, such as zooming cameras, is also addressed through developing an invariant-image servo controller. The controller allows for performing rendezvous maneuvers independently from the adjustments of the camera focal length, improving the performance and versatility of maneuvers. The stability of the proposed control scheme is proven analytically in the invariant space, and its viability is explored through numerical simulations.
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| 4. |
- Pomares, Jorge, et al.
(author)
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Spacecraft visual servoing with adaptive zooming for non-cooperative rendezvous
- 2018
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In: 2018 IEEE Aerospace Conference. - : IEEE Computer Society. - 9781538620144
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Conference paper (peer-reviewed)abstract
- The utilization of zooming cameras during a non-cooperative rendezvous in space is investigated in this paper. An image-based controller, utilizing visual servoing techniques usually applied to ground-based robotic systems, is designed for the particular problem of far-to-close approach of a spacecraft to a non-cooperative object. The controller directly utilizes the visual features from image frames of the noncooperative target for computing both attitude and orbital maneuvers concurrently. The additional feature derived from the utilization of the zooming camera gives a greater versatility to the maneuvers if compared with the classic fixed optics approaches. The stability of the proposed controller is proven analytically in the invariant space, and its viability is explored through the application to a realistic space debris removal scenario
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