Research on manipulator grasping method based on vision

Yang, Lei; Wu, Shi-Liang; Lv, Zhenlong; Lu, F.

doi:10.1051/matecconf/202030904004

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The best way to explain a gripper is to think of man's palm. Like a hand, a gripper may hold, tighten, handle, and release an object [1]. A gripper is an automatic device part.…”

Section: Introductionmentioning

confidence: 99%

“…Grippers also operate with hard automation for assembling; micro assembling, machining and packaging. Nowadays, deployment of vision based smart gripper for material handling in industrial applications remains challenging and ongoing research [1,3]. As Internet of Things (IoT) becomes more commercialized, various concept of IoT have been integrated with the gripper due to efficient usage.…”

Section: Introductionmentioning

confidence: 99%

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Development of Vision Based Smart Gripper for Material Handling Using Internet of Things

Ali

Zainur

Elshaikh

et al. 2022

J. Phys.: Conf. Ser.

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Robotic grippers have becoming an emerging trend due to their boundless applications in industrial automation. Nowadays, the deployment of vision based smart gripper for material handling in industrial applications remains challenging and ongoing research. As the Internet of Things (IoT) becomes more commercialized, the various concept of IoT have been integrated with the gripper due to efficient usage. Therefore, this project proposes the development of vision-based sensor of smart gripper for material handling in industrial applications that integrates with the IoT. The rationale of integrating IoT to vision based smart gripper is that it allows authenticated users to log in from any device, anywhere, and view video or images from vision based smart gripper in real-time for critical material handling. This system incorporates a vision sensor camera that acts as an “eye” to automatically detect and recognize the object with different weights and shapes and send the information to the robot for the next task. This smart gripper adopts a force sensor mounted into the fingertip to control the force applied when working with a wide range of objects with different weights. As for the electronic system, power module, communication and control module, sensor and actuator as well as user interface module have been adopted and integrated into the system. In the software development system, user interface configuration was developed through mobile application in which it communicates with Raspberry Pi B+ camera to serve as IoT platform. A series of experiments shows that the vision based gripper using IoT able to detect and recognize the objects and then send the information/command directly to the robot to execute grasping and lifting phase of the object to the desired location that has been assigned.

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“…The best way to explain a gripper is to think of man's palm. Like a hand, a gripper may hold, tighten, handle, and release an object [1]. A gripper is an automatic device part.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Vision Based Smart Gripper for Material Handling Using Internet of Things

Ali

Zainur

Elshaikh

et al. 2022

J. Phys.: Conf. Ser.

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A 3D Vision-Based Conveyor Tracking System for Pick-and-Sort Robotic Applications

Yang

Zeng

et al. 2021

2021 6th International Conference on Robotics and Automation Engineering (ICRAE)

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Position and Pose Measurement of 3-PRS Ankle Rehabilitation Robot Based on Deep Learning

Chen

Zhou

Huang

et al. 2022

RACSC

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Introduction: The position and pose measurement of the rehabilitation robot plays a very important role in patient rehabilitation movement, and the non-contact real-time robot position and pose measurement is of great significance. Rehabilitation training is a relatively complicated process, so it is very important to detect the training process of the rehabilitation robot in real time and accuracy. The method of the deep learning has a very good effect on monitoring the rehabilitation robot state. Methods: The structure sketch and the 3D model of the 3-PRS ankle rehabilitation robot are established, and the mechanism kinematics is analyzed to obtain the relationship between the driving input - the three slider heights - and the position and pose parameters. The whole network of the position and pose measurement is composed of two stages: (1) measuring the slider heights using the CNN based on the robot image and (2) calculating the position and pose parameter using the BPNN based on the measured slider heights from the CNN. According to the characteristics of continuous variation of the slider heights, a regression CNN is proposed and established to measure the robot slider height. Based on the data calculated by using the inverse kinematics of the 3-PRS ankle rehabilitation robot, a BPNN is established to solve the forward kinematics for the position and pose. Results: The experimental results show that the regression CNN outputs the slider height and then the BPNN accurately outputs the corresponding position and pose. Eventually, the position and pose parameters are obtained from the robot image. Compared with the traditional robot position and pose measurement method, the proposed method has significant advantages. Conclusion: The proposed 3-PRS ankle rehabilitation position and pose method can not only shorten the experiment period and cost, but also get excellent timeliness and precision. The proposed approach can help the medical staff to monitor the status of the rehabilitation robot and help the patient rehabilitation in training. Discussion: The goal of the work is to construct a new position and pose detection network based on the combination of the regression convolutional neural network (CNN) and the back propagation neural network (BPNN). The main contribution is to measure the position and pose of the 3-PRS ankle rehabilitation robot in real time, which improves the measurement accuracy and the efficiency of the medical staff work.

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Research on manipulator grasping method based on vision

Cited by 3 publications

References 5 publications

Development of Vision Based Smart Gripper for Material Handling Using Internet of Things

Development of Vision Based Smart Gripper for Material Handling Using Internet of Things

A 3D Vision-Based Conveyor Tracking System for Pick-and-Sort Robotic Applications

Position and Pose Measurement of 3-PRS Ankle Rehabilitation Robot Based on Deep Learning

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