Grasping and Positioning Tasks for Selective Compliant Articulated Robotic Arm Using Object Detection and Localization: Preliminary Results

Farag, Mohannad; Ghafar, Abdul; Alsibai, Mohammed Hayyan

doi:10.1109/iceee2019.2019.00061

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Using visual perception, deep learning has been applied in robotic pick-and-place tasks with various objects (i.e., deformable objects, cluttered objects, and mixed variety of objects). [26][27][28][29][30][31][32] Single depth camera, [33][34][35] depth sensors, [36] and 3D sensors [37] have been used to obtain visual data about the objects and their environment. Visual data-driven grasping techniques have relied heavily on machine learning models.…”

Section: Doi: 101002/aisy202300533mentioning

confidence: 99%

Hyper‐Versatile Gripping: Synergizing Mechanical and Machine Intelligence of a Hybrid Robotic Gripper

Khin,

Yeow,

Ang

2024

Advanced Intelligent Systems

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Integration of robotic solutions in manufacturing sector is growing. However, it is still concentrated in certain industries (i.e., electronics and automotive) where standardization of product physical form is high. Current state‐of‐the‐art gripping solutions fall short when they need to accommodate items with high variability in physical form. This challenging scenario for automation can be found in a few industries (i.e., e‐commerce). Automation of pick‐and‐place processes in this area requires a more versatile gripping solution. To resolve this challenge, this article proposes a novel way to improve grip‐versatility by synergizing the mechanical and machine intelligence of a hybrid robotic gripper (HRG). Comparative analysis with commercial grippers shows that HRG can pick a more diverse range of items with success rate 94.78%. Visual perception‐based picking strategy is developed to automate the reconfiguration of HRG into a stable grasp pose for different objects. Using the proposed reconfigurable picking strategy, the efficacy of HRG in pick‐and‐place tasks is evaluated using three parameters—mean pick per hour (MPPH), successful execution over total attempts (SETA), and average cycle time (AVGCT). HRG can effectively pick items in cluttered workspace with MPPH of 98.54 ± 15.49, SETA of 0.93 ± 0.11, and AVGCT of 34.76 ± 3.31 s.

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Section: Doi: 101002/aisy202300533mentioning

confidence: 99%

Hyper‐Versatile Gripping: Synergizing Mechanical and Machine Intelligence of a Hybrid Robotic Gripper

Khin,

Yeow,

Ang

2024

Advanced Intelligent Systems

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“…Robotic arms are now practical in many applications [ 22 , 23 ]. The robotic arms can be divided into many types (for example, the Cartesian robotic arm type, the gantry arm type, the cylindrical arm type, the spherical arm type, and the selective compliance assembly robot arm (SCARA) type, among others [ 24 ]).…”

Section: Electromagnetic Radiation (Emr) Side-channel Analysis (Sca) ...mentioning

confidence: 99%

Improvement of Electromagnetic Side-Channel Information Measurement Platform

Yuan

Liu

2023

Sensors

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Research has shown that when a microcontroller (MCU) is powered up, the emitted electromagnetic radiation (EMR) patterns are different depending on the executed instructions. This becomes a security concern for embedded systems or the Internet of Things. Currently, the accuracy of EMR pattern recognition is low. Thus, a better understanding of such issues should be conducted. In this paper, a new platform is proposed to improve EMR measurement and pattern recognition. The improvements include more seamless hardware and software interaction, higher automation control, higher sampling rate, and fewer positional displacement alignments. This new platform improves the performance of previously proposed architecture and methodology and only focuses on the platform part improvements, while the other parts remain the same. The new platform can measure EMR patterns for neural network (NN) analysis. It also improves the measurement flexibility from simple MCUs to field programmable gate array intellectual properties (FPGA-IPs). In this paper, two DUTs (one MCU and one FPGA-MCU-IP) are tested. Under the same data acquisition and data processing procedures with similar NN architectures, the top1 EMR identification accuracy of MCU is improved. The EMR identification of FPGA-IP is the first to be identified to the authors’ knowledge. Thus, the proposed method can be applied to different embedded system architectures for system-level security verification. This study can improve the knowledge of the relationships between EMR pattern recognitions and embedded system security issues.

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“…Top view camera for workspace area [5,11] There are many applications where a top view of the 2D camera is practical and can work well, like picking good fruits from damaged fruits or sorting color-coded boxes or objects. However, the detection is only based on color, which can put the robot at risk of false detection because there is no accurate recognition of the type of object being manipulated.…”

Section: Figure (2)mentioning

confidence: 99%

Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning

Mohammad,

Kishore,

Prasetyo

2023

J. of App. Sci. & Adv. Eng.

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Localizing and recognition of objects are critical problems for indoor manipulation tasks. This paper describes an algorithm based on computer vision and machine learning that does object detection and gripping tasks. Detection of objects is carried out using a combination of a camera and depth sensor using a Kinect v1 depth sensor. Moreover, machine learning algorithms (YOLO) are used for computer vision. The project presents a method that allows the Kinect sensor to detect objects' 3D location. At the same time, it is attached to any robotic arm base, allowing for a more versatile and compact solution to be used in stationary places using industrial robot arms or mobile robots. The results show an error of locating an object to be 5 mm. and more than 70% confidence in detecting objects correctly. There are many possibilities in which this project can be used, such as in industrial fields, to sort, load, and unload different kinds of objects based on their type, size, and shape. In agriculture fields, to collect or sort different kinds of fruits, in kitchens and cafes where sorting objects like cups, bottles, and cans can occur. Also, this project can be added to mobile robots to do indoor human services or collect trash from different places.

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Grasping and Positioning Tasks for Selective Compliant Articulated Robotic Arm Using Object Detection and Localization: Preliminary Results

Cited by 14 publications

References 10 publications

Hyper‐Versatile Gripping: Synergizing Mechanical and Machine Intelligence of a Hybrid Robotic Gripper

Hyper‐Versatile Gripping: Synergizing Mechanical and Machine Intelligence of a Hybrid Robotic Gripper

Improvement of Electromagnetic Side-Channel Information Measurement Platform

Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning

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