coal mine robot; path planning; membrane computing; Informed RRT* algorithm; MC-IRRT* algorithm; multi-step search; parallel search

Abstract

In order to solve the problem of inaccurate estimation and poor robustness of image Jacobian matrix based on traditional Kalman filtering (KF) in the uncalibrated visual servo control of mine intelligent inspection robot, Kalman filtering algorithm with long and short-term memory (LSTM) (KFLSTM algorithm) is proposed. The KFLSTM algorithm uses the LSTM to compensate for the estimation error generated by the KF algorithm, uses the filter gain error, state estimation vector error and observation error for online training of the LSTM, and uses the trained LSTM model for optimal estimation of the Jacobian matrix to improve the real-time and robustness of visual servo control by improving the accuracy and stability of the Jacobian matrix estimation. The uncalibrated visual servo model based on the KFLSTM algorithm is established, and the most optimal Jacobian matrix is used as the input of the controller, which makes the controller output more accurate joint angular velocity so as to control the real-time operation of the manipulator. The KFLSTM algorithm is applied to the six-degree-of-freedom visual servo simulation experiment of the mine intelligent inspection robot. The results show that the image error convergence speed obtained by the KFLSTM algorithm is 100%-142% higher than that of the traditional KF algorithm, the image characteristic error is smaller, the positioning precision is 0.5 pixels, and the robot end effector moves smoothly. Moreover, the method has strong anti-noise interference capability, which can improve the precision and efficiency of the mine intelligent inspection robot effectively and enhance its stability and safety.

Keywords

• mine intelligent inspection robot; uncalibrated visual servoing; image jacobian matrix; kalman filtering; long and short-term memory networks