Fault Restoration of Six-Axis Force/Torque Sensor Based on Optimized Back Propagation Networks

Six-axis force/torque sensors are widely installed in manipulators to help researchers achieve closed-loop control. When manipulators work in comic space and deep sea, the adverse ambient environment will cause various degrees of damage to F/T sensors. If the disability of one or two dimensions is restored by self-restoration methods, the robustness and practicality of F/T sensors can be considerably enhanced. The coupling effect is an important characteristic of multi-axis F/T sensors, which implies that all dimensions of F/T sensors will influence each other. We can use this phenomenon to speculate the broken dimension by other regular dimensions. Back propagation neural network (BPNN) is a classical feedforward neural network, which consists of several layers and adopts the back-propagation algorithm to train networks. Hyperparameters of BPNN cannot be updated by training, but they impact the network performance directly. Hence, the particle swarm optimization (PSO) algorithm is adopted to tune the hyperparameters of BPNN. In this work, each dimension of a six-axis F/T sensor is regarded as an element in the input vector, and the relationships among six dimensions can be obtained using optimized BPNN. The average MSE of restoring one dimension and two dimensions over the testing data is 1.1693 x 10(-5) and 3.4205 x 10(-5), respectively. Furthermore, the average quote error of one restored dimension and two restored dimensions are 8.800 x 10(-3) and 8.200 x 10(-3), respectively. The analysis of experimental results illustrates that the proposed fault restoration method based on PSO-BPNN is viable and practical. The F/T sensor restored using the proposed method can reach the original measurement precision.

Automated summary

Six-axis force/torque sensors are widely used in manipulators, but they are easily damaged in adverse environments. Self-restoration methods can enhance the robustness and practicality of the sensors. The coupling effect allows inferring the damaged dimension based on other regular dimensions. By using the particle swarm optimization algorithm, the hyperparameters of BPNN can be tuned to establish relationships among the dimensions. The proposed PSO-BPNN fault restoration method proves to be viable and practical, restoring the F/T sensor to its original measurement precision.