Visual servoing control of 4-DOF palletizing robotic arm for vision based sorting robot system

Cong, Vo Duy

doi:10.1007/s12008-022-01077-8

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…The second major issue should take into account is tracking moving targets for visual servoing. Motion target vision tracking plays an important role in several fields, such as space object in-orbit capture and tracking [23,24], unmanned aerial vehicle tracking and landing [25,26] and cargo sorting on assembly line [27]. Classified according to the feedback signal, visual servo tracking can be divided into positionbased visual servo tracking and image-based servo visual tracking.…”

Section: Introductionmentioning

confidence: 99%

Image moment-based visual positioning and robust tracking control of ultra-redundant manipulator

Li,

Zhou,

Zhu

et al. 2024

J Intell Robot Syst

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Image moment features can describe more general target patterns and have good decoupling properties. However, the image moment features that control the camera’s rotation motion around the x-axis and y-axis mainly depend on the target image itself. In this paper, the ultra-redundant manipulator visual positioning and robust tracking control method based on the image moments are advocated.First, six image moment features used to control camera motion around the x-axis and around the y-axis are proposed. And then, a novel method is proposed to use to select image features. For tracking a moving target, a kalman filter combined with adaptive fuzzy sliding mode control method is proposed to achieve tracking control of moving targets, which can estimate changes in image features caused by the target’s motion on-line and compensate for estimation errors. Finally, the experimental system based on Labview-RealTime system and ultra-redundant manipulator is used to verify the real-time performance and practicability of the algorithm. Experimental results are presented to illustrate the validity of the image features and tracking method.

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Section: Introductionmentioning

confidence: 99%

Image moment-based visual positioning and robust tracking control of ultra-redundant manipulator

Li,

Zhou,

Zhu

et al. 2024

J Intell Robot Syst

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show abstract

“…In recent decades, using human workers in manufacturing and industries has been replaced by automated machines and robots. Robots appear increasingly in factories, enterprises, and workshops to take on many different jobs [1][2][3][4][5]. To work in an automation system, a robot is often programmed to perform a repetitive task.…”

Section: Introductionmentioning

confidence: 99%

Control the robot arm through vision-based human hand tracking

Phuong,

Cong

2024

FME Transactions

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In this paper, hand tracking based on computer vision is developed to control the movement of a SCARA robot arm. The robot arm will move according to the movement of the human hand. Instead of using buttons on the teach-pendant or a computer control program to move the robot arm, the robot can now be easily controlled and positioned quickly by the movement of the operator's hand. A SCARA robot arm with two rotation joints and one translation motion is constructed for the validation system. Two states of the hand are recognized for controlling the vacuum cup to grasp the products. Stepper motors drive the robot arm. Arduino Uno is used as the main controller for controlling the stepper motors. The handtracking is performed by using the MediaPipe Hands framework developed by Google. The coordinates of 21 hand landmarks are extracted for further processing. A program is written on a personal computer to process the image to get the position and state of the hand. This position is transformed into the rotation angles of the robot's joints. Then, the angles and state are sent to the Arduino board. The Arduino board creates pulse signals to rotate the stepper motors. The experimental results show that the robot's trajectory is close to the hand trajectory at a low speed.

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“…Robot manipulators have extensive application in various fields, such as the manufacturing industry [1], sorting systems [2], quadruped robots [3], and rehabilitation exoskeletons [4]. Recently, its trajectory-tracking control has received significant attention from researchers.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Super-Twisting Sliding Mode Control for Robot Manipulators with Input Saturation

Jing,

Zhang,

Liu

et al. 2024

Sensors

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The paper investigates a modified adaptive super-twisting sliding mode control (ASTSMC) for robotic manipulators with input saturation. To avoid singular perturbation while increasing the convergence rate, a modified sliding mode surface (SMS) is developed in this method. Using the proposed SMS, an ASTSMC is developed for robot manipulators, which not only achieves strong robustness but also ensures finite-time convergence. The boundary of lumped uncertainties cannot be easily obtained. A modified adaptive law is developed such that the boundaries of time-varying disturbance and its derivative are not required. Considering input saturation in practical cases, an ASTSMC with saturation compensation is proposed to reduce the effect of input saturation on tracking performances of robot manipulators. The finite-time convergence of the proposed scheme is analyzed. Through comparative simulations against two other sliding mode control schemes, the proposed method has been validated to possess strong adaptability, effectively adjusting control gains; simultaneously, it demonstrates robustness against disturbances and uncertainties.

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Visual servoing control of 4-DOF palletizing robotic arm for vision based sorting robot system

Cited by 10 publications

References 25 publications

Image moment-based visual positioning and robust tracking control of ultra-redundant manipulator

Image moment-based visual positioning and robust tracking control of ultra-redundant manipulator

Control the robot arm through vision-based human hand tracking

Adaptive Super-Twisting Sliding Mode Control for Robot Manipulators with Input Saturation

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