Abstract
As an essential perceptual device, the tactile sensor can efficiently improve robot intelligence by providing contact force perception to develop algorithms based on contact force feedback. However, current tactile grasping technology lacks high-performance sensors and high-precision grasping prediction models, which limits its broad application. Herein, an intelligent robot grasping system that combines a highly sensitive tactile sensor array was constructed. A dataset that can reflect the grasping contact force of various objects was set up by multiple grasping operation feedback from a tactile sensor array. The stability state of each grasping operation was also recorded. On this basis, grasp stability prediction models with good performance in grasp state judgment were proposed. By feeding training data into different machine learning algorithms and comparing the judgment results, the best grasp prediction model for different scenes can be obtained. The model was validated to be efficient, and the judgment accuracy was over 98% in grasp stability prediction with limited training data. Further, experiments prove that the real-time contact force input based on the feedback of the tactile sensor array can periodically control robots to realize stable grasping according to the real-time grasping state of the prediction model.
Highlights
In recent years, machine learning put forward decades ago has been growing rapidly and has received substantial research interest with the popularity of artificial intelligence [1,2,3]
We propose a two-finger gripper-based grasping system, which has simple structures and achieves good performance on stable grasping benefitted from the tactile sensor array
A robot grasping system that can grasp objects stably in a tactile sensing range by combining a highly sensitive tactile sensor array with a high motion resolution robot arm is proposed in this work
Summary
Machine learning put forward decades ago has been growing rapidly and has received substantial research interest with the popularity of artificial intelligence [1,2,3]. Based on the analysis above, the effective combination of the robot arm and the high sensitivity and high spatial resolution of the tactile sensor is the hardware foundation for a flexible and precise grasping operation. The prediction model based on a machine learning algorithm can judge the actual grasping state by training the dataset to adjust the control parameters of the robot, achieving stable grasping operation. The realization of stable grasping refers to finding an appropriate pressure distribution based on the tactile sensor array, which reflects the force situation of the contact area between the object and the clamp on the end-effector of the robot. The select model guarantees high precision and efficiency, and practically avoids redundant judgement on grasping state It achieves judging grasping state periodically and adjusting the control force in actual grasping operations, based on the real-time feedback of the tactile sensor array
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