Automatic robot path integration using three-dimensional vision and offline programming

Bedaka, Amit Kumar; Vidal, Joel; Lin, Chyi‐Yeu

doi:10.1007/s00170-018-03282-w

Cited by 41 publications

(11 citation statements)

References 45 publications

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“…This feature is extremely beneficial in enabling color image information to be used to implement image processing. Recently, Amit et al [35] showed that the Kinect v1 can be used for highly demanding precision operations and complex manufacturing tasks. In addition, many studies have also indicated that the second generation of Kinect sensors has advantages over the first version in terms of performance, accuracy, and a wider field of view (FOV) [36,37].…”

Section: System Architecturementioning

confidence: 99%

Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs

Lin

2019

Sensors

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Random bin-picking is a prominent, useful, and challenging industrial robotics application. However, many industrial and real-world objects are planar and have oriented surface points that are not sufficiently compact and discriminative for those methods using geometry information, especially depth discontinuities. This study solves the above-mentioned problems by proposing a novel and robust solution for random bin-picking for planar objects in a cluttered environment. Different from other research that has mainly focused on 3D information, this study first applies an instance segmentation-based deep learning approach using 2D image data for classifying and localizing the target object while generating a mask for each instance. The presented approach, moreover, serves as a pioneering method to extract 3D point cloud data based on 2D pixel values for building the appropriate coordinate system on the planar object plane. The experimental results showed that the proposed method reached an accuracy rate of 100% for classifying two-sided objects in the unseen dataset, and 3D appropriate pose prediction was highly effective, with average translation and rotation errors less than 0.23 cm and 2.26°, respectively. Finally, the system success rate for picking up objects was over 99% at an average processing time of 0.9 s per step, fast enough for continuous robotic operation without interruption. This showed a promising higher successful pickup rate compared to previous approaches to random bin-picking problems. Successful implementation of the proposed approach for USB packs provides a solid basis for other planar objects in a cluttered environment. With remarkable precision and efficiency, this study shows significant commercialization potential.

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Section: System Architecturementioning

confidence: 99%

Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs

Lin

2019

Sensors

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“…The overall structure is shown in figure 8. Offline testing is through offline planning and simulation, from code generation to the final output program [20,21]. Still, the CNC system is expensive, the processing is the main task, and testing is supplemented.…”

Section: Inspection Path Planningmentioning

confidence: 99%

Research and Analysis Based on Three-dimensional Precision Inspection Process Planning Technology

Sun

Bai

2022

J. Phys.: Conf. Ser.

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Three-dimensional precision inspection has the characteristics of digitization, completeness, and timeliness. The manufacturing industry needs to research and improve the existing inspection process planning technology. Carried out research on the key technology of three-dimensional inspection process planning, including inspection information extraction, inspection point planning, path planning, etc., compared and analyzed the offline inspection technology represented by the coordinate measuring machine and the online inspection technology represented by the machining center. Studied and analyzed the system architecture, main functional modules of process planning software tools for these two types of inspections; finally, it looks forward to the future development trend.

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“…In another development, a 3D‐vision based offline path programming system using spatial data from CAD models has been proposed and developed. It is the culmination of features from OLP platforms and a simplistic 3D vector space depicting IDE [31]. In another work, a sequential guidance platform for the design and programming of generic robots tasked with handling items and structures is proposed.…”

Section: Industrial Revolutionsmentioning

confidence: 99%

A sequential roadmap to Industry 6.0: Exploring future manufacturing trends

et al. 2021

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It has been speculated that by the year 2050, technology will have progressed to the point of complete autonomy. This paper scrolls through patent pathways and intellectual developments throughout the industrial revolutions listing significant products and services that landmarked each revolution up to Industry 4.0. The patent trails and the recent IPR inputs are expected to assist readers in fast‐tracking up to speed on the bleeding edge of the current research pools while having an eagle's eye perspective on the previous developments so far. The research pools of Industry 4.0 are classified and explored. A lack of Human‐machine workforce synergy in Industry 4.0 and the nascent “customized manufacturing” concept is addressed in subsequent sections. The paper classifies two expected phases of Industry 5.0, highlighting the subdomains touted to be its focal areas. Lastly, Industry 5.0's niche research areas are checked and a suitable pathway to achieve the goals set for the sixth revolution is proposed.

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Automatic robot path integration using three-dimensional vision and offline programming

Cited by 41 publications

References 45 publications

Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs

Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs

Research and Analysis Based on Three-dimensional Precision Inspection Process Planning Technology

A sequential roadmap to Industry 6.0: Exploring future manufacturing trends

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