Attitude recovery from feature tracking for estimating angular rate of non-cooperative spacecraft

Biondi, Gabriele; Mauro, Stefano; Mohtar, Tharek; Pastorelli, Stefano Paolo; Sorli, Massimo

doi:10.1016/j.ymssp.2016.06.017

Cited by 24 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No markers were placed on the athletes’ suits nor on the equipment, due to the competition context. Some techniques for motion analysis based on features detection and point tracking are described in [13], [14], [15]. Video-recordings and image analysis were possible by means of a markerless analysis system based on digital cameras, working at a frequency of 90 fps.…”

Section: Subject and Methodsmentioning

confidence: 99%

Analysis of the pushing phase in Paralympic cross-country sit-skiers – Class LW10

Gastaldi

Mauro

Pastorelli

2016

Journal of Advanced Research

View full text Add to dashboard Cite

show abstract

Section: Subject and Methodsmentioning

confidence: 99%

Analysis of the pushing phase in Paralympic cross-country sit-skiers – Class LW10

Gastaldi

Mauro

Pastorelli

2016

Journal of Advanced Research

View full text Add to dashboard Cite

show abstract

“…The shown algorithm is a slight modification of the original SALSA. This variation, which considers the presence of the sensing matrix Γ is extensively explained in [26] The presented approach can be used for recovering each of the quaternion components, under the assumption that they are mostly sparse in some domain. The motion of space debris can be considered as a periodic and slow succession of finite rotations.…”

Section: B Dealing With Occlusionsmentioning

confidence: 99%

“…CADET aims at the functional development of major technologies for the capture and removal of space debris. Within this project, the team of Politecnico di Torino is involved in the localization of the center of mass of the reference Targets [25], in the estimation of their rotational dynamic state [26], and in the selection and verification of the Chaser guidance and control strategies, based on an in-the-loop simulator [27].…”

mentioning

confidence: 99%

Control of a Noncooperative Approach Maneuver Based on Debris Dynamics Feedback

Corpino

Mauro

Pastorelli

et al. 2018

Journal of Guidance, Control, and Dynamics

View full text Add to dashboard Cite

Space Debris removal is a critical issue related to space research. One of the key requirements for a removal mission is the assessment of the target rotational dynamics. Ground observations are not sufficient for reaching the accuracy level required to guide the chaser spacecraft during the capture maneuver. Moreover, the guidance and control strategy for the chaser to approach the target is a critical aspect of such missions. This paper present simulation results of two complementary methods, one for estimating the entire rotational dynamic state of the target, and the other for accurately controlling the approach maneuver. In particular, the information coming from the identification and prediction of the actual motion of the rotation axis of the target is exploited by the second method for aligning the docking interface of the chaser with that axis at the instant of capture. The dynamic estimation is based on Kalman filtering in an original combination with compressive sampling techniques for making the method robust to failures of the observation sensors. The guidance of the chaser is based on a model predictive control law. The combined simulation of the employment of the methods has revealed the feasibility of the global approach. I. .IntroductionNE of the main concerns in the space field is the high number of objects orbiting the Earth in orbits of interest for the accomplishment of scientific and communication missions. Currently, more than 10000 objects bigger than 10 cm take up Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) [1].

show abstract

“…However, some spacecrafts or sub-modules could be out-of-control in-orbit due to fault and fuel consumption. 5 Considering cost and safety, it is necessary to repair them by the autonomous servicing spacecraft. 1 During the process, the autonomous spacecraft will capture and dock with an out-of-control target, and the two spacecrafts therefore become a combined spacecraft.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based inertia tensor identification of the combined spacecraft

Chu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

The identification accuracy of inertia tensor of combined spacecraft, which is composed by a servicing spacecraft and a captured target, could be easily affected by the measurement noise of angular rate. Due to frequently changing operating environments of combined spacecraft in space, the measurement noise of angular rate can be very complex. In this paper, an inertia tensor identification approach based on deep learning method is proposed to improve the ability of identifying inertia tensor of combined spacecraft in the presence of complex measurement noise. A deep neural network model for identification is constructed and trained by enough training data and a designed learning strategy. To verify the identification performance of the proposed deep neural network model, two testing set with different ranks of measure noises are used for simulation tests. Comparison tests are also delivered among the proposed deep neural network model, recursive least squares identification method, and tradition deep neural network model. The comparison results show that the proposed deep neural network model yields a more accurate and stable identification performance for inertia tensor of combined spacecraft in changeable and complex operating environments.

show abstract

Attitude recovery from feature tracking for estimating angular rate of non-cooperative spacecraft

Cited by 24 publications

References 21 publications

Analysis of the pushing phase in Paralympic cross-country sit-skiers – Class LW10

Analysis of the pushing phase in Paralympic cross-country sit-skiers – Class LW10

Control of a Noncooperative Approach Maneuver Based on Debris Dynamics Feedback

Deep learning-based inertia tensor identification of the combined spacecraft

Contact Info

Product

Resources

About