Ellipse detection-based visual servo control for capturing upper-stage rocket body

Kojima, Hirohisa; Okawara, Taku; Trivailo, Pavel M.

doi:10.1016/j.actaastro.2021.02.015

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…There are basically two types of methods to tune an appropriate dynamic servo gain. The first approach is to realize through classical control methods [31][32][33][34]. A PID-based adaptive visual servoing method was proposed in [31], but its generalization capability to unstructured environments was weak.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

Li,

Peng,

et al. 2023

Asian Journal of Control

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In this study, a novel image‐based visual servo (IBVS) controller for robot manipulators is investigated using an optimized extreme learning machine (ELM) algorithm and an offline reinforcement learning (RL) algorithm. First of all, the classical IBVS method and its difficulties in accurately estimating the image interaction matrix and avoiding the singularity of pseudo‐inverse are introduced. Subsequently, an IBVS method based on ELM and RL is proposed to solve the problem of the singularity of the pseudo‐inverse solution and tune adaptive servo gain, improving the servo efficiency and stability. Specifically, the ELM algorithm optimized by particle swarm optimization (PSO) was used to approximate the pseudo‐inverse of the image interaction matrix to reduce the influence of camera calibration errors. Then, the RL algorithm was adopted to tune the adaptive visual servo gain in continuous space and improve the convergence speed. Finally, comparative simulation experiments on a 6‐DOF robot manipulator were conducted to verify the effectiveness of the proposed IBVS controller.

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Section: Introductionmentioning

confidence: 99%

“…The effectiveness of the proposed method has been validated in a 6-DOF robot manipulator. A gain-switching algorithm was proposed by [34]. This method first converges the projection ellipse to the circle and then adjusts the relative distance with forward movement.…”

Section: Introductionmentioning

confidence: 99%

Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

Li,

Peng,

et al. 2023

Asian Journal of Control

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“…On the other hand, trajectories can be directly planned in image space, by establishing a Jacobian matrix mapped from image features to the kinematic model of the manipulator, visual servoing is accomplished according to the change of the image. 43,44 Obviously, those methods' performance is affected by the quality of the image, particularly in space environment.…”

Section: Introductionmentioning

confidence: 99%

A collision-free visual servoing method for two space manipulators capturing tumbling satellites

Yang

Wei

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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A novel visual servoing method is proposed to synchronously capture a malfunctional satellite’s docking ring by two space manipulators. Due to the lack of conspicuous visual marks, structured light is implemented to estimate the ring’s pose. To ensure the capturing process’s security, two aspects are investigated. Spatially, capturing target points for two manipulators are planned together by considering the chaser’s orientation. As a consequence, collision risk between two manipulators is eliminated. Temporally, by adding duration parameters in trajectory generation algorithm, capturing synchronism for two manipulators is guaranteed. Experimental results show that, by the proposed method, a tumbling target was successfully captured by two space manipulators. Compared to existing methods, the collision risk between two manipulators is thoroughly eliminated and the capturing synchronism is improved remarkably.

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“…Vision, as the most important means of acquiring information for humans, is also considered the most important perceptual capability for robots. The images obtained from vision sensors can be used as feedback information to construct a closed-loop control of the robot, i.e., vision servo [9][10]. It is different from what is commonly referred to as machine vision.…”

Section: Introductionmentioning

confidence: 99%

Research on image recognition and processing of motion targets of warehouse logistics robots

Zhao,

Zhou,

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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In developing robots for warehouse logistics, image recognition and processing for moving targets are the cornerstone of subsequent work. In this paper, the Meanshift algorithm is extended to continuous image sequences, and the Camshift algorithm for motion target tracking in a warehouse environment is proposed to obtain effective tracking of targets through the probability distribution when the color of continuous images changes dynamically. Based on target tracking, a feature-matching-based image recognition method is constructed. The scene image is first treated with improved Gamma correction for light equalization, and then image features are extracted using SURF feature points. Regarding running time, the feature matching method is, on average, 2.03 seconds faster than FLDA and 0.96 seconds faster than PCAFLDA under the same external conditions. By optimizing the computational structure, the feature-matching method can address the need for efficiency in warehouse logistics.

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Ellipse detection-based visual servo control for capturing upper-stage rocket body

Cited by 5 publications

References 22 publications

Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

A collision-free visual servoing method for two space manipulators capturing tumbling satellites

Research on image recognition and processing of motion targets of warehouse logistics robots

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