A Deep Learning Strategy For On-Orbit Servicing Via Space Robotic Manipulator

Stolfi, A.; Angeletti, Federica; Gasbarri, Paolo; Panella, Massimo

doi:10.1007/s42496-019-00028-z

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Unlike on-ground operations, where the base of the robotic arm is fixed, in a space environment for onorbit applications, the robotic arm is connected to the service spacecraft, which is floating, and as a result, the motion of the arm influences the translational and rotation dynamics of the base platform. Such dynamic coupling of the robotic arm and the spacecraft dynamics complicates the reconfiguration of the arm in the desired orientation [88,89]. The deployment of the robotic arm follows the pre-grasping phase of the mission, where the relative translational and angular velocities between the service and the debris spacecraft is reduced as close to zero as possible so that the end effector of the robotic arm can maintain stable contact with the debris.…”

Section: On-orbit Servicing/actuationmentioning

confidence: 99%

Resiliency in Space Autonomy: a Review

et al. 2023

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Purpose of Review: The article provides an extensive overview on the resilient autonomy advances made across various missions, orbital or deep-space, that captures the current research approaches while investigating the possible future direction of resiliency in space autonomy. Recent Findings: In recent years, the need for several automated operations in space applications has been rising, that ranges from the following: spacecraft proximity operations, navigation and some station keeping applications, entry, decent and landing, planetary surface exploration, etc. Also, with the rise of miniaturization concepts in spacecraft, advanced missions with multiple spacecraft platforms introduce more complex behaviours and interactions within the agents, which drives the need for higher levels of autonomy and accommodating collaborative behaviour coupled with robustness to counter unforeseen uncertainties. This collective behaviour is now referred to as resiliency in autonomy. As space missions are getting more and more complex, for example applications where a platform physically interacts with non-cooperative space objects (debris) or planetary bodies coupled with hostile, unpredictable, and extreme environments, there is a rising need for resilient autonomy solutions. Summary Resilience with its key attributes of robustness, redundancy and resourcefulness will lead toward new and enhanced mission paradigms of space missions.

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Section: On-orbit Servicing/actuationmentioning

confidence: 99%

Resiliency in Space Autonomy: a Review

et al. 2023

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“…6 Stolfi presented a deep neural network (DNN) control method for a space manipulator system and analyzed the characteristics. 7 Liu proposed a probabilistic ensemble neural network for the dynamic estimation of free-floating space manipulators (FFSMs), and it is capable of predicting long-term behavior. 3 She combined a quantum-interference artificial neural network into the space manipulator controller to realize the error tracking and compensation.…”

Section: Introductionmentioning

confidence: 99%

Dynamics-based deep learning and fractional-order fixed-time sliding mode control for space robot impedance system

Zhao

Sun

Zhu

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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To complete complex tasks with space robots in the impedance environment, sliding mode control (SMC) has been proposed and plays an important role in the past decades. Learning-based control has also met with great success in robotics and has been closely followed in the control of space robots. However, as an interdisciplinary, only a few studies have combined deep learning and traditional controller in the space robot directly. This paper proposes a novel fractional-order fixed-time SMC for impedance control of space robots, taking advantage of a model-based deep learning approach to obtain the dynamics of space robots. The learning results are used to make predictions for the data-based robot dynamics. The fractional-order fixed-time SMC controller is added to that data-based model, and it ensures the finite closed-loop convergence time. Small-dataset training is more reasonable in the situation of space robots, which reduces training time and sampling size. Compared to traditional methods, combining deep learning and fractional-order fixed-time SMC provides better tracking performance and improves closed-loop robustness. It accomplishes the control target in the impedance model using the estimated data-driven parameters. Theoretical analysis proves the closed-loop behavior, and simulation results verify its effectiveness.

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“…Recently, methodologies based on data analysis and information extraction, in the broad field of machine learning, are being increasingly used to address damage/failure identification problems to achieve a wider range of applicability [ 37 , 38 ]. In order to overcome the limitations associated to traditional neural networks solutions [ 39 ], such as real-world noise, more complex deep learning (DL) models and techniques, with higher generalisation capabilities, have been introduced as data extractors, classifiers, and predictors [ 40 , 41 , 42 ]. Such models can include also recurrent neural networks (RNN) [ 43 ] to efficiently obtain the information from time-series data.…”

Section: Introductionmentioning

confidence: 99%

A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning

Angeletti

Iannelli

Gasbarri

et al. 2022

Sensors

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Most modern Earth and Universe observation spacecraft are now equipped with large lightweight and flexible structures, such as antennas, telescopes, and extendable elements. The trend of hosting more complex and bigger appendages, essential for high-precision scientific applications, made orbiting satellites more susceptible to performance loss or degradation due to structural damages. In this scenario, Structural Health Monitoring strategies can be used to evaluate the health status of satellite substructures. However, in particular when analysing large appendages, traditional approaches may not be sufficient to identify local damages, as they will generally induce less observable changes in the system dynamics yet cause a relevant loss of payload data and information. This paper proposes a deep neural network to detect failures and investigate sensor sensitivity to damage classification for an orbiting satellite hosting a distributed network of accelerometers on a large mesh reflector antenna. The sensors-acquired time series are generated by using a fully coupled 3D simulator of the in-orbit attitude behaviour of a flexible satellite, whose appendages are modelled by using finite element techniques. The machine learning architecture is then trained and tested by using the sensors’ responses gathered in a composite scenario, including not only the complete failure of a structural element (structural break) but also an intermediate level of structural damage. The proposed deep learning framework and sensors configuration proved to accurately detect failures in the most critical area or the structure while opening new investigation possibilities regarding geometrical properties and sensor distribution.

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A Deep Learning Strategy For On-Orbit Servicing Via Space Robotic Manipulator

Cited by 11 publications

References 10 publications

Resiliency in Space Autonomy: a Review

Resiliency in Space Autonomy: a Review

Dynamics-based deep learning and fractional-order fixed-time sliding mode control for space robot impedance system

A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning

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