Autonomous robotic capture of non-cooperative target by adaptive extended Kalman filter based visual servo

Dong, Gangqi; Zhu, Zheng H.

doi:10.1016/j.actaastro.2016.02.003

Cited by 67 publications

(14 citation statements)

References 26 publications

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“…To robustify image-based visual servoing, the extended Kalman filter may be employed to estimate feature motion in images [76]. Kalman filters are suited to visual object tracking [77] and an adaptive extended Kalman filter has been applied visual servoing of non-cooperative satellite targets for manipulator capture [78]. Intensity-based servoing offers robustness without the need for feature extraction or pose estimation [79].…”

Section: Space Manipulator Operations-evolution From Teleoperation Tomentioning

confidence: 99%

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery

2019

Robotics

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Space-based manipulators have traditionally been tasked with robotic on-orbit servicing or assembly functions, but active debris removal has become a more urgent application. We present a much-needed tutorial review of many of the robotics aspects of active debris removal informed by activities in on-orbit servicing. We begin with a cursory review of on-orbit servicing manipulators followed by a short review on the space debris problem. Following brief consideration of the time delay problems in teleoperation, the meat of the paper explores the field of space robotics regarding the kinematics, dynamics and control of manipulators mounted onto spacecraft. The core of the issue concerns the spacecraft mounting which reacts in response to the motion of the manipulator. We favour the implementation of spacecraft attitude stabilisation to ease some of the computational issues that will become critical as increasing level of autonomy are implemented. We review issues concerned with physical manipulation and the problem of multiple arm operations. We conclude that space robotics is well-developed and sufficiently mature to tackling tasks such as active debris removal.

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Section: Space Manipulator Operations-evolution From Teleoperation Tomentioning

confidence: 99%

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery

2019

Robotics

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“…It can provide more relaxed condition for follow-up tasks such as space debris capture and removal, autonomous rendezvous and docking, and orbiting observation and exploration. In general, active visual tracking is typically classified into two main categories: Position-based Visual Servoing (PBVS) [3], [11]- [13] and Image-based Visual Servoing (IBVS) [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

“…It can directly and naturally navigate chaser to the target with expected relative pose. Dong [3] proposed a PBVS scheme for space robotic manipulator to capture noncooperative object, in which photogrammetry and Adaptive Extended Kalman Filter (AEKF) were combined to estimate 6-DoF pose of target. However, this method was merely evaluated under an easy simulated configuration where the non-cooperative object possesses specific and discriminative image features with simple motion Fig.…”

Section: Introductionmentioning

confidence: 99%

Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning

Zhang¹,

Sun²,

Lei³

2021

Preprint

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Active visual tracking of space non-cooperative object is significant for future intelligent spacecraft to realise space debris removal, asteroid exploration, autonomous rendezvous and docking. However, existing works often consider this task into different subproblems (e.g. image preprocessing, feature extraction and matching, position and pose estimation, control law design) and optimize each module alone, which are trivial and sub-optimal. To this end, we propose an end-to-end active visual tracking method based on DQN algorithm, named as DRLAVT. It can guide the chasing spacecraft approach to arbitrary space non-cooperative target merely relied on color or RGBD images, which significantly outperforms position-based visual servoing baseline algorithm that adopts state-of-the-art 2D monocular tracker, SiamRPN. Extensive experiments implemented with diverse network architectures, different perturbations and multiple targets demonstrate the advancement and robustness of DRLAVT. In addition, We further prove our method indeed learnt the motion patterns of target with deep reinforcement learning through hundreds of trial-and-errors.

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“…Various approaches have been investigated to estimate health parameters for the purpose of performance monitoring, such as weighted least squares, 6 Kalman filters, 7,8 stochastic modeling, 9,10 neural networks, expert systems, and genetic algorithms. 11–14 Among these methodologies, Kalman filters have attracted much attention due to their simplicity, robustness, and suitability for real-time implementation. 7,15 Linearized Kalman filter (LKF) is an optimal estimator for engine health monitoring relying on a linear model of gas turbine.…”

Section: Introductionmentioning

confidence: 99%

Gas turbine performance monitoring based on extended information fusion filter

Lü

Huang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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Performance monitoring is a critical issue for gas turbine engine for improving the operation safety and reducing the maintenance cost. With regard to this, variants of Kalman-filters-based state estimation have been employed to detect gas turbine performance, but the classical centralized Kalman filters are subject to heavy computational effort and poor fault tolerance. A novel nonlinear fusion filter algorithm using information description with distributed architecture is proposed and applied to gas turbine performance monitoring. This methodology is developed from federated Kalman filter, and a bank of local extended information filters and one information mixer are combined with extended information fusion filter. The local state estimates and covariance calculated in parallel by the local extended information filters are integrated in the information mixer to yield a global state estimate. The global state estimate of nonlinear system is fed back to the local filters with weighted factor for next iteration. The aim of the proposed methodology is to reduce the computational efforts of state estimation and improve robustness to sensor faults in cases of gas turbine performance monitoring. The simulation results on a turbofan engine confirm the extended information fusion filter's effective capabilities in comparison to the general central ones.

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Autonomous robotic capture of non-cooperative target by adaptive extended Kalman filter based visual servo

Cited by 67 publications

References 26 publications

Tutorial Review on Space Manipulators for Space Debris Mitigation

Tutorial Review on Space Manipulators for Space Debris Mitigation

Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning

Gas turbine performance monitoring based on extended information fusion filter

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