Development of a lower extremity wearable exoskeleton with double compact elastic module: preliminary experiments

Yi Long, Zhi-jiang Du, Chao-feng Chen, Wei-dong Wang, Wei Dong
2017 Mechanical Sciences  
<p><strong>Abstract.</strong> 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
more » ... ompliance 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<span class="thinspace"></span>mm and an inner hole with a diameter of 12<span class="thinspace"></span>mm, while in the DEM, the torsional spring has the outer circle with a diameter of 80<span class="thinspace"></span>mm and the inner circle with a diameter of 16<span class="thinspace"></span>mm. The torsional spring in the PEM has a thickness of 5<span class="thinspace"></span>mm and a weight of 60<span class="thinspace"></span>g, while that in the DEM has a thickness of 10<span class="thinspace"></span>mm and a weight of 80<span class="thinspace"></span>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<span class="thinspace"></span>Nm<span class="thinspace"></span>rad<sup>−1</sup>, which is capable of withstanding a maximum torque of 4<span class="thinspace"></span>Nm, while that in the DEM has a stiffness of 80.2<span class="thinspace"></span>Nm<span class="thinspace"></span>rad<sup>−1</sup>, which is capable of withstanding a maximum torque of 30<span class="thinspace"></span>Nm. The experimental results and the simulation data show that the maximum resultant errors are 6<span class="thinspace"></span>% for the PEM and 4<span class="thinspace"></span>% 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.</p>
doi:10.5194/ms-8-249-2017 fatcat:7lupzsmavbafbf7esrpl5jjy2q