Studies have developed various types of soft robotic gloves for hand rehabilitation in recent years. Most soft actuators achieved a sufficient thumb flexion assist while lacking opposition support, which requires the coordination of thumb flexion and abduction-adduction. The difficulties for thumb support lie in the intrinsic complexity of thumb movements and spatial restriction of the hand. To realize multiple degrees of freedom of the thumb and make effective use of the limited space of the hand's dorsal side, we optimized and compared two approaches for thumb support. The combination approach used two independent soft actuators for thumb flexion and abduction-adduction support, respectively. The all-in-one approach used one single soft actuator to assist motions in different directions. We designed the soft actuators for each approach based on the thumb's biomechanical characteristics and evaluated their thumb flexion, abduction support performance in terms of the range of motion (RoM) and force output, and the opposition support performance using an enhanced Kapandji test. The results showed a larger abduction RoM and force output of the composition approach and a higher Kapandji score of the all-in-one approach, indicating that the two approaches might be applicable for thumb support but have the advantage in different rehabilitation stages.
To provide a stable surgical view in Minimally Invasive Surgery (MIS), it is necessary for a flexible endoscope applied in MIS to have adjustable stiffness to resist different external loads from surrounding organs and tissues. Pneumatic soft actuators are expected to fulfill this role, since they could feed the endoscope with an internal access channel and adjust their stiffness via an antagonistic mechanism. For that purpose, it is essential to estimate the external load. In this study, we proposed a neural network (NN)-based active load-sensing scheme and stiffness adjustment for a soft actuator for MIS support with antagonistic chambers for three degrees of freedom (DoFs) of control. To deal with the influence of the nonlinearity of the soft actuating system and uncertainty of the interaction between the soft actuator and its environment, an environment exploration strategy was studied for improving the robustness of sensing. Moreover, a NN-based inverse dynamics model for controlling the stiffness of the soft actuator with different flexible endoscopes was proposed too. The results showed that the exploration strategy with different sequence lengths improved the estimation accuracy of external loads in different conditions. The proposed method for external load exploration and inverse dynamics model could be used for in-depth studies of stiffness control of soft actuators for MIS support.
Pneumatic artificial muscles (PAMs) have been widely applied to robotic systems, especially assistive devices, which could benefit from PAMs' intrinsic viscoelasticity. However, nonlinearity and hysteresis make it challenging to achieve high-accuracy control. Using pre-tensioned springs to replace one side PAMs in an antagonistic structure is one efficient way to improve control accuracy. However, in the previous study, the selection method for spring, especially the range of spring constant has not been sufficiently investigated. This study designed a method to find the optimal spring constant based on gradient descent algorithm; and verified it with prototype and simulation experiments. Results showed that the group with optimal spring constant leads to a faster response time, smaller overshoot, and lower steady-state error, which indicated that the proposed method could find optimal spring constant. Moreover, the sensitivity of PAMs dynamic model parameters to the optimal spring stiffness was investigated. This study provides insights to practical use of soft actuators.
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