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Journal ArticleDOI

Minimum Base Disturbance Control of Free-Floating Space Robot during Visual Servoing Pre-capturing Process

01 Apr 2020-Robotica (Cambridge University Press)-Vol. 38, Iss: 4, pp 652-668
TL;DR: A method named off-line optimizing visual servoing algorithm is innovatively proposed to minimize the base disturbance during the visual Servoing process where the degrees-of-freedom of the manipulator is not enough for a zero-reaction control.
Abstract: During visual servoing space activities, the attitude of free-floating space robot may be disturbed due to dynamics coupling between the satellite base and the manipulator. And the disturbance may cause communication interruption between space robot and control center on earth. However, it often happens that the redundancy of manipulator is not enough to fully eliminate this disturbance. In this paper, a method named off-line optimizing visual servoing algorithm is innovatively proposed to minimize the base disturbance during the visual servoing process where the degrees-of-freedom of the manipulator is not enough for a zero-reaction control. Based on the characteristic of visual servoing process and the robot system modeling, the optimal control method is applied to achieve the optimization, and a pose planning method is presented to achieve a second-order continuity of quaternion getting rid of the interruption caused by ambiguity. Then simulations are carried out to verify the method, and the results show that the robot is controlled with optimized results during visual servoing process and the joint trajectories are smooth.
Citations
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Journal ArticleDOI
TL;DR: Consistent optimization results in terms of the six-order and seven-order polynomial in both cases prove the PSO algorithm can be effectively used for joint trajectory planning of SMRS.
Abstract: This paper investigates the application of particle swarm optimization (PSO) algorithm to plan joint trajectories of the space modular reconfigurable satellite (SMRS). SMRS changes its configuration by joint motions to complete various space missions; its movement stability is affected by joints motions because of the dynamic coupling effect in space. To improve the movement stability in reconfiguration progress, this paper establishes the optimization object equation to characterize the movement stability of SMRS in its reconfiguration process. The velocity-level and position-level kinematic models based on the proposed virtual joint coordinate system of SMRS are derived. The virtual joint coordinate system solves the problem of asymmetric joint coordinate system resulted by the asymmetric joint arrangement of SMRS. The six-order and seven-order polynomial curves are chosen to parameterize the joint trajectories and ensure the continuous position, velocity, and acceleration of joint motions. Finally, PSO algorithm is used to optimize the trajectory parameters in two cases. Consistent optimization results in terms of the six-order and seven-order polynomial in both cases prove the PSO algorithm can be effectively used for joint trajectory planning of SMRS.

8 citations


Cites background from "Minimum Base Disturbance Control of..."

  • ...For example, severe pose disturbance may cause communication interruption between satellite and ground control center [10]....

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Journal ArticleDOI
TL;DR: A repetitive learning sliding mode stabilization control is proposed to stabilize the flexible base–link–joint space robot capture system and ensures that the base attitude and joints of the system reach the desired trajectories in a limited time after capturing, obtain better control quality, and suppress the multiple flexible vibrations of the base, links and joints.
Abstract: During the process of satellite capture by a flexible base–link–joint space robot, the base, joints, and links vibrate easily and also rotate in a disorderly manner owing to the impact torque. To address this problem, a repetitive learning sliding mode stabilization control is proposed to stabilize the system. First, the dynamic models of the fully flexible space robot and the captured satellite are established, respectively, and the impact effect is calculated according to the motion and force transfer relationships. Based on this, a dynamic model of the system after capturing is established. Subsequently, the system is decomposed into slow and fast subsystems using the singular perturbation theory. To ensure that the base attitude and the joints of the slow subsystem reach the desired trajectories, link vibrations are suppressed simultaneously, and a repetitive learning sliding mode controller based on the concept of the virtual force is designed. Moreover, a multilinear optimal controller is proposed for the fast subsystem to suppress the vibration of the base and joints. Two sub-controllers constitute the repetitive learning sliding mode stabilization control for the system. This ensures that the base attitude and joints of the system reach the desired trajectories in a limited time after capturing, obtain better control quality, and suppress the multiple flexible vibrations of the base, links and joints. Finally, the simulation results verify the effectiveness of the designed control strategy.

8 citations

Journal ArticleDOI
TL;DR: In this article , a direct visual servoing algorithm for control of a space-based two-arm manipulator is proposed, where one of the arms performs the manipulation and the second arm is dedicated to the observation of the target zone of manipulation.

3 citations

Journal ArticleDOI
TL;DR: In this paper , the authors divide the active debris capture removal (RA-ADCR) technology progress history into three periods and present the status of related research, and two major development trends are summarized and subdivided through the analysis and collation of research achievements over the past three years.
Abstract: Space is the driving force of the world’s sustainable development, and ensuring the sustainability of human activity in space is also necessary. Robotic arm active debris capture removal (RA-ADCR) is a noteworthy technology for containing the dramatic increase in space debris and maintaining orbital safety. This review divides the RA-ADCR technology progress history into three periods and presents the status of related research. Two major development trends are summarized and subdivided through the analysis and collation of research achievements over the past three years. Taking the treatment of parameter uncertainties as the entry point, researchers would like to improve the discrimination accuracy and scope to reduce uncertainties. On the other hand, researchers accept such uncertainties and would like to offset and avoid the impact of uncertainties by extending the error margins. Subsequently, the challenges of RA-ADCR are analyzed in line with the task execution flow, which mainly focuses on the conflict between on-satellite computing power and the performance of task execution. In addition, feasible solutions for the current phase are discussed. Finally, future outlooks are evaluated and discussed.

3 citations

Journal ArticleDOI
24 Feb 2022-Robotica
TL;DR: In this paper , a fixed-time trajectory tracking control of a free-flying rigid space manipulator perturbed by model uncertainties and external disturbances is investigated, and a novel robust fixed time integrated controller is developed by integrating a nominal fixed-times proportional differential-like controller with a fixed time disturbance observer.
Abstract: This article investigates the fixed-time trajectory tracking control of a free-flying rigid space manipulator perturbed by model uncertainties and external disturbances. A novel robust fixed-time integrated controller is developed by integrating a nominal fixed-time proportional–differential-like controller with a fixed-time disturbance observer. It is strictly proved that the proposed controller can ensure the position and velocity tracking errors regulate to zero in fixed time even subject to lumped disturbance. Benefiting from the feedforward compensation, the proposed controller has the strong robustness and excellent disturbance attenuation capability. The effectiveness and advantages of the proposed control approach are validated through simulations and comparisons.

2 citations

References
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Book
01 Jan 1970

3,442 citations

Book
20 Nov 2008
TL;DR: Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control, suitable for use in senior undergraduate and graduate courses in automation and computer, electrical, electronic and mechanical engineering courses with strong robotics content.
Abstract: The classic text on robot manipulators now covers visual control, motion planning and mobile robots too! Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. The text develops around a core of consistent and rigorous formalism with fundamental and technological material giving rise naturally and with gradually increasing difficulty to more advanced considerations. The theory of manipulator structures presented in the early part of the book encompasses: the fundamentals: kinematics, statics and trajectory planning; and the technology of actuators, sensors and control units. Subsequently, more advanced instruction is given in: dynamics and motion control of robot manipulators; mobile robots; motion planning; and interaction with the environment using exteroceptive sensory data (force and vision). Appendices ensure that students will have access to a consistent level of background in basic areas such as rigid-body mechanics, feedback control, and others. Problems are raised and the proper tools established to find engineering-oriented solutions rather than to focus on abstruse theoretical methodology. To impart practical skill, more than 60 examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, nearly 150 end-of-chapter problems are proposed, and the book is accompanied by a solutions manual (downloadable from www.springer.com/978-1-84628-641-4) containing the MATLAB code for computer problems; this is available free of charge to those adopting Robotics as a textbook for courses. This text is suitable for use in senior undergraduate and graduate courses in automation and computer, electrical, electronic and mechanical engineering courses with strong robotics content.

2,305 citations

Journal ArticleDOI
30 Nov 2006
TL;DR: This paper is the first of a two-part series on the topic of visual servo control using computer vision data in the servo loop to control the motion of a robot using basic techniques that are by now well established in the field.
Abstract: This paper is the first of a two-part series on the topic of visual servo control using computer vision data in the servo loop to control the motion of a robot. In this paper, we describe the basic techniques that are by now well established in the field. We first give a general overview of the formulation of the visual servo control problem. We then describe the two archetypal visual servo control schemes: image-based and position-based visual servo control. Finally, we discuss performance and stability issues that pertain to these two schemes, motivating the second article in the series, in which we consider advanced techniques

2,026 citations

01 Jan 2012

406 citations


"Minimum Base Disturbance Control of..." refers background in this paper

  • ...Because the sampling period is short, the simplification is shown as follows: JG = JG0 + J̇G0t + o(t) ≈ JG0 + J̇G0t (21)...

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  • ...To finish the pose plan, it is necessary to map Ψ back to Q by the following equations: q = ( cos φ 2 sin φ 2 nT )T (13) ωe = [ I3×3 + 1 − cos φ φ (n×) + φ − sin φ φ (n× n×) ] ψ̇ (14) αe = [ φ̇ φ2 (φ sin φ − 2 + 2 cos φ) (n×) + φ̇ φ2 (3 sin φ − φ cos φ − 2φ) (n× n×) ] ψ̇ + φ − sin φ φ2 ψ̇ × (n × ψ̇) + [ I3×3 + 1 − cos φ φ (n×) + φ − sin φ φ (n× n×) ] ψ̈ (15) By virtue of Eqs....

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  • ...(20), the following conclusion can be obtained: ẋ = A(t)x + B(t)u + ξ (24) where x = θ̇ and A(t), B(t), and ξ are defined by the following equations: A(t) = − (JG0 + J̇G0t)† [ ∂ ( J̇G0x ) ∂x ] x0 − D (JG0 + J̇G0t)† (JG0 + J̇G0t) (25) B(t) = [ I − (JG0 + J̇G0t)† (JG0 + J̇G0t)] (26) ξ = (JG0 + J̇G0t)† · [ãd + Dṽd + K f(sd, se0 + Cṽe0t) − J̇G0x0] + (JG0 + J̇G0t)† [ ∂ ( J̇G0x ) ∂x ] x0 x0 (27) 4.2.2....

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  • ...(39) can be employed as the cost function of optimal control method.30 To apply the optimal method, the Hamiltonian can be defined as: H = 1 2 (Jbωx)TO(Jbωx) + 1 2 u(t)TRu(t) + λT(Ax + Bu + ξ) (40) where all the variables except O and R are the function of time t , and H is the functional of x, u, and λ. Suppose x∗, u∗, and λ∗ are the optimal state vector, control vector, and Lagrange multipliers, respectively, then: ẋ∗ = ∂ H ∂λ = Ax∗ + Bu∗ + ξ (41) λ̇∗ = −∂ H ∂x = −JTbωOJbωx∗ − ATλ∗ (42) 0 = ∂ H ∂u = Ru∗ + BTλ∗ (43) According to Donald E. Kirk, it is reasonable to obtain λ∗ from the following equation:30 λ∗ = Kx∗ + γ (44) then Eqs....

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  • ...Because the sampling period is short, the simplification is shown as follows: JG = JG0 + J̇G0t + o(t) ≈ JG0 + J̇G0t (21) se = ⎛ ⎜⎝ pe0 + ve0t + o(t)[ 1 + 1 2 ωe0t + o(t) ] ⊗ qe0 ⎞ ⎟⎠≈ ⎛ ⎜⎝ pe0 + ve0t qe0 + 1 2 ωe0 ⊗ qe0t ⎞ ⎟⎠ = se0 + [ 03×3 03×1 04×3 c(q) ] ṽe0t = se0 + Cṽe0t (22) where o(·) represents the higher order of the corresponding variable, 0s denote the zero matrix, and c(q) is the matrix that can convert angular velocity to the differential of quaternion, which is defined by Eq....

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