LAUSR

This study proposes an external force estimation method based on the error probability model and a sliding window variational adaptive Kalman filter (SWVAKF) for the problem of industrial robot external force estimation. A probability model with a normal distribution was provided, in which the covariance was linearly related to the joint velocity to address the uncertainty in the estimation error of the friction model. A normal distribution probability model was presented to address the time-varying characteristics of the contact external force when the robot interacts with the environment, in which the covariance of the derivative term of the external force was linearly related to the estimated external force. The robot interaction process was dynamically modeled using generalized momentum and external force as state variables, friction estimation deviation and external force derivative term as random interference, and generalized momentum with normally distributed noise as the measurement output. The parameters of the probability model of random interference items were determined by analyzing the experimental data of system identification, and the covariance related to joint velocity and external force was obtained. A set of adaptive scale factors related to the joint velocity and estimated force change rate when the robot interacts with the environment is proposed, and then the error covariance estimated based on the error probability model and the SWVAKF algorithm is fused by the adaptive scale factor, and a covariance of friction deviation and external force derivative term in the robot interaction process and an external force estimation method were provided. Experimental results reveal that the proposed method reduces the root-mean-square (RMS) error by 17.07% compared to the standard Kalman external force estimation method and has a better external force estimation effect.