Estimation of Deformation for Self-balancing Lower Limb Exoskeleton Only Using Force/Torque Sensors

Abstract

This paper presents a general estimation method of deformation for the self-balancing lower limb exoskeleton (SBLLE). In particular, we propose a Bi-LSTM deformation estimator (BLDE) to predict and compensate for the deformation of SBLLE based on the current force and torque data measured by force/torque (F/T) sensors. First, we choose four movements including squatting down and up, waist twisting, left foot lifting, and right foot lifting to mimic the constituent action of walking motion. The deformation data set is obtained through the motion capture analysis system and offline planning trajectories, and the relative F/T data set is obtained by the F/T sensors embedded in the feet of SBLLE. Second, the BiLSTM network is trained to learn the relationship between the deformation and F/T and verified on the test set. After that, BLDE is added to the control system of SBLLE to estimate and compensate for the deformation. Finally, four same movements and the walking experiment are conducted on the exoskeleton AutoLEE-G2 with BLDE. The experimental results have proven that BLDE can predict and compensate for deformation by only using F/T sensors.