MLP Neural Network-Based Precise Localization of Robot Assembly Parts

Cheng, Bin; Li, Zonggang; Jiao, Jianjun; An, Guanglin

doi:10.1007/978-981-99-6480-2_50

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…They employed the gradient descent approach for this estimation. Cheng, Li, Jiao, and An [13] used PBVS (posture-Based Visual Servoing) to estimate the posture of a robot and navigate around barriers. The authors used the aversion torque approach to determine the manipulators that are repulsive inverse kinematics, yielding results that were characterized by speed, flexibility, and accuracy.…”

Section: Navigation Control Modulementioning

confidence: 99%

Development and Evaluation of a Computer Vision System for Robot Navigation and Object Recognition in Real-World Environments

Helgo

2024

JCNS

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The article discusses the vision framework for computing that includes image recognition, classification, prioritization, and navigation control modules. In this framework, a user model is used to feed the robotic controllers, whose performance improves in dynamic virtual contexts. In contrast, the vision module uses a multi-level perceptual neural network capable of efficient image segmentation, object recognition, and color segmentation, using the control module Position-Based Vision Serving (PBVS) and actions such as Avoid Collision (), Go-Ahead (), and Follow( ). It controls the motion of the robot, so the system successfully tested and met the requirements of the Antimedia Robotics Pioneer I robot. In addition, it was consistent with real life. The results show the effectiveness of the system in providing effective guidance and avoiding obstacles. Furthermore, the study investigates the use of artificial neural networks for image recognition and classification. In addition, it requires the use of SpCoMapping to add language maps to useful information. In summary, studies have emphasized the potential of computer vision and neural networks to improve robotic communication and language learning.

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Section: Navigation Control Modulementioning

confidence: 99%

Development and Evaluation of a Computer Vision System for Robot Navigation and Object Recognition in Real-World Environments

Helgo

2024

JCNS

View full text Add to dashboard Cite

show abstract

MLP Neural Network-Based Precise Localization of Robot Assembly Parts

Cited by 1 publication

References 17 publications

Development and Evaluation of a Computer Vision System for Robot Navigation and Object Recognition in Real-World Environments

Development and Evaluation of a Computer Vision System for Robot Navigation and Object Recognition in Real-World Environments

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