Fig. 5: Real-time dynamic slip sensation and prevention of intelligent robot based on AOAN and closed-loop control program.

a Diagram of the AOAN on robot gripper and the slip motion of a metal ball. b The slip recognition process and the implementation of slip prevention action upon slippage. c Camera image of the slip motion process of ball that passes through four sensors in sequence from top to bottom. d Real-time signal recording for a series of object manipulations according to the algorithm (gripping, slipping, slip prevention and shaking). i: the gripper holding the ball securely with no slippage happens; ii: the slippage of ball when the closed-loop algorithm is not activated; iii: the gripper implements slip recognition and prevention actions when the closed-loop algorithm is activated; iv: after implementing slip prevention program, the secure grip at even shakes the gripper. e Illustration of CNN in which a LSTM layer is followed by a fully connected layer. Bottom shows the internal architecture of standard LSTM cell. f Evolution of the recognition accuracy with training epochs for different AOAN systems and the ideal numeric, respectively. g Probability of three possible results after inputting instances of AOAN with OEST and without OEST into the trained neural network.