Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning

Muslikhin, Muslikhin; Horng, Jenq-Ruey; Yang, Szu-Yueh; Wang, Ming‐Shyan

doi:10.1109/access.2021.3129474

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…For objects with complex shapes, irregular surface structures or uneven textures, traditional grasp strategies are no longer applicable. Therefore, many scholars have carried out different researches on the improvement and optimization of recognition algorithm for irregular object [9][10][11][12]. Poss et al developed a modular algorithm to achieve a successful grasping rate of 72% when handling various types of express packages, including dirty, labeled, or damaged ones [13].…”

Section: Introductionmentioning

confidence: 99%

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

Yang,

Lin,

et al. 2024

Meas. Sci. Technol.

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In the application of vision measurement, the black light-absorbing object is difficult to reflect the structured light from infrared emitter of the RGB-D camera. Therefore, an image recognition algorithm based on reference environment information is proposed to acquire the spatial positioning information of black volutes in the depalletizing system. The hardware system of the depalletizing system is mainly constructed of an upper computer, a six-axis industrial robot, an RGB-D camera and an end adsorption device. Firstly, the horizontal position information of each volute placed on the cardboard is obtained by the depth differences between the cardboard and the volute. Then, the depth information of the volute is obtained by the upper cardboard depth through collecting the position of the end vacuum suction cup triggered by feedback signal from vacuum generator. Secondly, a regional planar hand-eye calibration method is developed to improve the calibration accuracy in two-dimensional coordinates. The regional calibration method divides the robot working area into four regions: upper left, lower left, upper right, and lower right. The transformation matrix of each region is calculated separately. Finally, the depalletizing experiment is conducted on the three types of volutes. It is concluded that the average positioning error of the grasping center point of each volute obtained by our method is 3.795 mm, and its standard deviation is 1.769 mm. The average value of regional planar hand-eye calibration error is 4.044 mm, and its standard deviation is 1.501 mm. Under a stack of materials with dimensions of 1350 mm × 1350 mm × 1500 mm, the maximum error is controlled within 15mm. Additionally, when combined with the end feedback compensation mechanism, the success rate for grasping all three volutes reaches 100%.

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Section: Introductionmentioning

confidence: 99%

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

Yang,

Lin,

et al. 2024

Meas. Sci. Technol.

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An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5

et al. 2023

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Navigating Unstructured Space: Deep Action Learning-Based Obstacle Avoidance System for Indoor Automated Guided Vehicles

Aryanti,

Wang,

Muslikhin

2024

Electronics

Self Cite

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Automated guided vehicles (AGVs) have become prevalent over the last decade. However, numerous challenges remain, including path planning, security, and the capacity to operate safely in unstructured environments. This study proposes an obstacle avoidance system that leverages deep action learning (DAL) to address these challenges and meet the requirements of Industry 4.0 for AGVs, such as speed, accuracy, and robustness. In the proposed approach, the DAL is integrated into an AGV platform to enhance its visual navigation, object recognition, localization, and decision-making capabilities. Then DAL itself was introduced to combine the work of You Only Look Once (YOLOv4), speeded-up robust features (SURF), and k-nearest neighbor (kNN) and AGV control in indoor visual navigation. The DAL system triggers SURF to differentiate two navigation images, and kNN is used to verify visual distance in real time to avoid obstacles on the floor while searching for the home position. The testing findings show that the suggested system is reliable and fits the needs of advanced AGV operations.

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Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning

Cited by 4 publications

References 47 publications

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5

Navigating Unstructured Space: Deep Action Learning-Based Obstacle Avoidance System for Indoor Automated Guided Vehicles

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