Camila Taira

Control remains a challenge in precision applications in robotics, particularly when combined with execution in small time intervals. This study employed a two-degree-of-freedom (2-DoF) planar robotic arm driven by a detailed human musculoskeletal model for actuation, incorporating nonlinear control techniques to execute a precision task through simulation. Then, we compared these simulations with real experimental data from healthy subjects performing the same task. Our results show that the Feedback Linearization Control (FLC) applied performed satisfactorily within the task execution constraints compared to a robust nonlinear control technique, i.e., Sliding Mode Control (SMC). On the other hand, differences can be observed between the behavior of the simulated model and the real experimental data, where discrepancies in terms of errors were found. The model errors increased with the amplitude and remained unchanged with any increase in the task execution frequency. However, in human trials, the errors increased both with the amplitude and, notably, with a drastic rise in frequency.

Exoskeletons have been introduced in industrial environments to prevent overload or repetitive stress injuries in workers. However, due to the lack of public detailed information about most of the commercial exoskeletons, it is necessary to further assess their load capacity and evolution over time, as their performance may change with use. We present the design and construction of a controlled device to measure the torque of industrial exoskeletons, along with the results of static and dynamic testing of an exoskeleton model. A step motor in the test bench moves the exoskeleton arm in a pre-defined path at a prescribed speed. The force measured with a beam load cell located at the interface between the exoskeleton arm and the test bench is used to derive the torque. The proposed test bench can be easily modified to allow different exoskeleton models to be tested under the same conditions.