Robot dynamics modeling method based on deep learning

A dynamic modeling and deep learning technology, applied in the field of robot dynamic modeling based on deep learning, can solve the problems that the echo state characteristics cannot be fully satisfied, the output feedback connection is ignored, and the input signal characteristics are not fully considered.

Active Publication Date: 2018-10-09
CAPITAL NORMAL UNIVERSITY
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Problems solved by technology

However, the ESN machine learning algorithm does not fully consider the characteristics of the input signal, usually ignoring the output feedback connection
Therefore, the echo s

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  • Robot dynamics modeling method based on deep learning
  • Robot dynamics modeling method based on deep learning
  • Robot dynamics modeling method based on deep learning

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Embodiment Construction

[0056] The specific implementation method of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0057] The invention provides a method for precise control of a robot dynamics model, which uses a deep learning method to model robot dynamics, and performs motion control and behavior prediction in the field of intelligent robots. In order to learn from long-term sequence data, the deep learning method based on GRU (GateRecurrent Unit gated recurrent unit) in RNN (Recurrent Neutral Network) is used to model the dynamics of the robot. Provides guarantee for accurate motion control of learning models. The position, velocity and acceleration of each joint are input at the input end of the RNN neural network model, and the torque of each joint is obtained at the output end, and the next action is predicted to improve the motion control accuracy of the robot.

[0058] Different from the traditional feedforward network (Feedforward ...

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Abstract

The invention discloses a robot dynamics modeling method based on deep learning and belongs to the field of intelligent robots. Data are acquired and divided into a training set and a data set, and adynamics model is a built, and a recurrent neural network (RNN) is constructed; the training set is divided according to the time step and is input into an input hidden layer, and is converted into three-dimensional data to reach a GRU cell layer, currently input information is combined with previous information, and the proportion of state information, participated into a newly generated state, at the previous moment is calculated; and then a current candidate state obtained due to calculation and information of the previous time step moment are selected through an updating gate, a hidden layer state at the current moment is obtained, transmitted to a next time step, and output to an output hidden layer, and an acquired real result with a predicted value smaller than or equal to an errorthreshold is obtained and is an optimal value. Finally, the data set is utilized for detecting a gated recurrent unit (GRU) network. According to the method, the torque detecting precision is improved, the training time of an input signal is greatly shortened, and the gradient error of traditional counterpropagation is reduced.

Description

technical field [0001] The invention belongs to the field of intelligent robots, in particular to a robot dynamics modeling method based on deep learning. Background technique [0002] An important application of the robot dynamic model is to control the robot. The torque required for the robot movement can be accurately calculated through the dynamic equation; however, due to the influence of factors such as disturbance, elasticity, nonlinear friction, and load changes, the parameters of many dynamic models It is difficult to be determined. Traditional dynamics methods (such as Lagrangian, Newton Euler, and Kane) are difficult to accurately model the dynamics of robots, which cannot meet accurate practical applications. [0003] In recent years, the development of deep learning has ushered in a turning point in the solution of this problem; the neural network has a strong nonlinear mapping ability, and by training a certain amount of data, it is not necessary to artificiall...

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/1605
Inventor 邵振洲孙鹏飞渠瀛关永施智平王晓东
Owner CAPITAL NORMAL UNIVERSITY
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