Abstract. In this paper, a double compact elastic module is designed and implemented in the lower extremity exoskeleton. The double compact elastic module is composed of two parts, i.e., physical human robot interaction (pHRI) measurement and the elastic actuation system (EAS), which are called proximal elastic module (PEM) and distal elastic module (DEM) respectively. The PEM is used as the pHRI information collection device while the DEM is used as the compliance device. A novel compact parallelogram-like structure based torsional spring is designed and developed. An iterative finite element analysis (FEA) based optimization process was conducted to find the optimal parameters in the search space. In the PEM, the designed torsional spring has an outer circle with a diameter of 60 mm and an inner hole with a diameter of 12 mm, while in the DEM, the torsional spring has the outer circle with a diameter of 80 mm and the inner circle with a diameter of 16 mm. The torsional spring in the PEM has a thickness of 5 mm and a weight of 60 g, while that in the DEM has a thickness of 10 mm and a weight of 80 g. The double compact elastic module prototype is embedded in the mechanical joint directly. Calibration experiments were conducted on those two elastic modules to obtain the linear torque versus angle characteristic. The calibration experimental results show that this torsional spring in the PEM has a stiffness of 60.2 Nm rad−1, which is capable of withstanding a maximum torque of 4 Nm, while that in the DEM has a stiffness of 80.2 Nm rad−1, which is capable of withstanding a maximum torque of 30 Nm. The experimental results and the simulation data show that the maximum resultant errors are 6 % for the PEM and 4 % for the DEM respectively. In this paper, an assumed regression algorithm is used to learn the human motion intent (HMI) based on the pHRI collection. The HMI is defined as the angular position of the human limb joint. A closed-loop position control strategy is utilized to drive the robotic exoskeleton system to follow the human limb's movement. To verify the developed system, experiments are performed on healthy human subjects and experimental results show that this novel robotic exoskeleton can help human users walk, which can be extended and applied in the assistive wearable exoskeletons.
A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots
