Pick-place of dynamic objects by robot manipulator based on deep learning and easy user interface teaching systems

Hossain, Delowar; Capi, Genci; Jindai, Mitsuru; Kaneko, Shin-ichiro

doi:10.1108/ir-05-2016-0140

Cited by 31 publications

(9 citation statements)

References 26 publications

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“…In addition, Hossain et al presented teaching systems that enable non-expert operators to pick up a specific object and place it at a target location. 142 A deep belief neural network (DBNN)-based approach which uses a captured image as its input has been proposed in ref. [143].…”

Section: Deep Learningmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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SUMMARY Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

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Section: Deep Learningmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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“…A brief discussion on existing multiple salient region detection methods is given in Section 2. 1. In recent times, deep learning-based end-to-end object recognition has been attempted for industrial applications a few times [8][9][10] with reasonable success. However, their performance, in general, relies heavily on accurate and vast labeled data [11], which are hard to generate for an industrial environment and vary from one environment to another.…”

Section: Introductionmentioning

confidence: 99%

Structure‐aware multiple salient region detection and localization for autonomous robotic manipulation

Bhuyan

Sen

Deb

2022

IET Image Processing

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This paper proposes a multiple salient region detection and localization approach for unstructured industrial robot work environments with arbitrarily located and orientated objects. Different from the existing, the authors' novel technique to detect multiple salient regions performs locally adaptive center‐surround operations on proto‐object partitions obtained through color consistency and spatial proximity analysis. The multi‐scale center‐surround operations are done by masks that are local structure‐aware yielding regions with precise and accurate boundaries as required for robotic manipulation. First, experiments to evaluate the multiple salient region detection performance are carried out using four standard databases having images with multiple salient objects. Quantitative result analysis using F‐measure, shuffled F‐measure, shuffled AUC and MAE, and subjective result inspection suggests that the proposed approach is in general better at collectively detecting multiple salient regions than the state‐of‐the‐art, including those based on deep learning. Then, real‐life experiments involving robotic manipulation are carried out to demonstrate the utility of the multiple salient region detection method. For robotic manipulation, object localization is improved after salient region detection by employing a fast shadow detection algorithm proposed based on hue analysis, and recognition through existing matching techniques is applied only at the localized salient regions. The benefit of the novel multiple salient region detection approach in the robotic manipulation system is shown using localization and pose estimation accuracy, rates of detection and recognition, positional and angular errors, and processing speed.

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“…Meanwhile, Hossain et al conducted research on the Deep Believes Neural Network (DBNN) to estimate the object's location [4]. A device called Kinect was deployed as a motion sensor.…”

Section: Introductionmentioning

confidence: 99%

3D Object Pose Estimation Using Chamfer Matching and Flexible CAD File Base

Sari¹,

Putranti²

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Estimating the object pose is an interesting topic in the industrial robotic vision field. By having an accurate result for detecting object pose, it means the system performs the task as the target in the bin-picking technique. The methods which are developed are varies widely. But the challenge for this paper is estimating a 3D object using mono camera accurately. The object which is used in this paper has the symmetric rotational shape, in this case is the sprayer. In this paper, the camera uses a tool from the Blender Software, such that the ground truth is measurable and it will be the reference for calculating the error. The applied algorithms of this paper are Border Line Extraction Algorithm utilized in the template generation step as the reference template, Directional Chamfer Matching for detecting the coarse pose, and Lavenberg-Marquardt Method to optimize the object pose result. The result achieves the average error of the coarse pose for x and y position (translation pose) are 2.05 mm and 0.71 mm. Meanwhile for the optimized pose, the average error for x and y position (translation pose) are 1.82 mm and 0.24 mm. Regarding the rotational pose, the average error of the rotational coarse pose with respect to x and z axis are 0.01 degree and 0.45 degree. Whereas the average error of the rotational optimized pose with respect to x and z axis are 2.88 degree and 0.82 degree.

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Pick-place of dynamic objects by robot manipulator based on deep learning and easy user interface teaching systems

Cited by 31 publications

References 26 publications

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Structure‐aware multiple salient region detection and localization for autonomous robotic manipulation

3D Object Pose Estimation Using Chamfer Matching and Flexible CAD File Base

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