Error recovery in the assembly of a Self-Organizing Manipulator by using active visual and force sensing

Xue, Guoqing; Fukuda, Toshio; Asama, Hajime

doi:10.1007/bf00711256

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…This procedure involved the placement of a six-axis force/torque sensor in the wrist of one manipulator and a CCD camera in the hand of another manipulator. The system aimed to replicate the operation of a human by combining a visual system, a manipulator, and a force/torque sensor into a unified hand-eye working system [56]. In the same year, in his paper titled "A three-dimensional machine-vision Approach for Automatic Robot Programming", D. Tsai explored the representation and simulation of collision behavior in manipulators.…”

Section: Mechatronicsmentioning

confidence: 99%

Machine Learning, Mechatronics, and Stretch Forming: A History of Innovation in Manufacturing Engineering

Grigoras,

Zichil,

Ciubotariu

et al. 2024

Machines

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This review focuses on the complex connections between machine learning, mechatronics, and stretch forming, offering valuable insights that can lay the groundwork for future research. It provides an overview of the origins and fundamentals of these fields, emphasizes notable progress, and explores the influence of these fields on society and industry. Also highlighted is the progress of robotics research and particularities in the field of sheet metal forming and its various applications. This review paper focuses on presenting the latest technological advancements and the integrations of these fields from their beginnings to the present days, providing insights into future research directions.

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Section: Mechatronicsmentioning

confidence: 99%

Machine Learning, Mechatronics, and Stretch Forming: A History of Innovation in Manufacturing Engineering

Grigoras,

Zichil,

Ciubotariu

et al. 2024

Machines

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“…Longer-horizon tasks, like stacking multiple objects, require awareness of intermediate action success. Research has addressed: reasoning about visual stability of objects in simulation [14]- [16] and in a robot workspace [17]; detecting intermediate task success [18], [19]; and predicting whether a task will succeed before executing it [20]. We define success in terms of two object relationships: in and on, and we assume oracle-level grasping and localization during action execution.…”

Section: Related Workmentioning

confidence: 99%

Improving Robot Success Detection using Static Object Data

Scalise

Thomason

Bisk

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We use static object data to improve success detection for stacking objects on and nesting objects in one another. Such actions are necessary for certain robotics tasks, e.g., clearing a dining table or packing a warehouse bin. However, using an RGB-D camera to detect success can be insufficient: same-colored objects can be difficult to differentiate, and reflective silverware cause noisy depth camera perception. We show that adding static data about the objects themselves improves the performance of an end-to-end pipeline for classifying action outcomes. Images of the objects, and language expressions describing them, encode prior geometry, shape, and size information that refine classification accuracy. We collect over 13 hours of egocentric manipulation data for training a model to reason about whether a robot successfully placed unseen objects in or on one another. The model achieves up to a 57% absolute gain over the task baseline on pairs of previously unseen objects.

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“…In the case of 3-dimensional measurement, the accuracy of image processing is depended very much on the viewpoint of camera [7]. Here, to measure the position of peg and hole, we move the cell and camera to make the camera perpendicular to the cell.…”

Section: B Measuring the Position Of Peg And Hole By Image Processingmentioning

confidence: 99%

“…We have proposed CEBOT (CEllular roBOTic Systems), and studied a kind of modular robotSelf Organizing Manipulator since 1990[ 1] [7]. In Fig.1 we show the concept of Self Organizing Manipulator.…”

Section: Introductionmentioning

confidence: 99%

Dynamically reconfigurable robotic system. Assembly of new type cells as a dual-peg-in-hole problem

Xue

Fukuda

Arai

et al.

Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics

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In this paper, we first introduce a new type cell(modu1e) of Self Organizing Manipulator that was developed by the authors, then present a method for assembling this type of cell using manipulators. Since the new type of cells has two guides for connection, it can also be considered as a dual-peg-in-hole configuration. To obtain effective assembly sequence of cells, we observe the sequence of work and record forceltorque data, psychological data of the people performing the same work, By analyzing the result of the experiment, we obtained a reasonable sequence for performing such work. We apply this result to the assembly of cells using a camera coordinated with a forcehorque sensor. The analysis of force and geometric condition in the DualPeg-in-Hole configuration, and results of experiments are also presented.

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Error recovery in the assembly of a Self-Organizing Manipulator by using active visual and force sensing

Cited by 9 publications

References 4 publications

Machine Learning, Mechatronics, and Stretch Forming: A History of Innovation in Manufacturing Engineering

Machine Learning, Mechatronics, and Stretch Forming: A History of Innovation in Manufacturing Engineering

Improving Robot Success Detection using Static Object Data

Dynamically reconfigurable robotic system. Assembly of new type cells as a dual-peg-in-hole problem

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