Intelligent grasping using neural modules

Valente, C.M.O.; Schammass, A.; Araújo, A.F.R.; Caurin, Glauco Augusto de Paula

doi:10.1109/icsmc.1999.816650

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Hu et al [61] developed an operation and grasp control system based on sensor-motor fusion for a robot hand-eye system, proposed a motion recognition method of a multifinger manipulator based on an AdaBoost-SVM, and proved the high response and flexibility of this method. Valente et al [62] used the competitive Hopfield neural network to collect several points on the edge of the object to build an approximate polygon, used the radial bases functionglobal ridge regression (RBF) network to process the polygon, and selected the appropriate grasping points to guide the grasping of the manipulator.…”

Section: A Support Vector Machine (Svm)mentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

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With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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Section: A Support Vector Machine (Svm)mentioning

confidence: 99%