Fiber Bragg grating-based sensor for torque and angle measurement in a series elastic actuator's spring

Area of Science:

• Robotics and Human-Robot Interaction
• Optical Sensing Technologies
• Mechanical Engineering

Background:

• Conventional torque and angle sensors for exoskeleton actuators are often bulky, prone to misalignment, and lack multiplexing capabilities.
• Fiber Bragg grating (FBG) sensors present a promising alternative due to their robustness and suitability for multi-parametric monitoring.
• Accurate measurement of torque and angle is crucial for human-robot interaction, particularly in series elastic actuators.

Purpose of the Study:

• To characterize and validate a novel Fiber Bragg grating (FBG)-based sensor system for measuring torque and angle variations.
• To assess the performance of FBG sensors in the context of a series elastic actuator's torsional spring.
• To investigate and mitigate temperature cross-sensitivity in FBG-based torque and angle measurements.

Main Methods:

• An experimental setup was designed incorporating a torsional spring and an array of three Fiber Bragg grating (FBG) sensors.
• The setup was subjected to repeated cycles of varying torques and angles to collect sensor response data.
• A non-strain FBG sensor was utilized to derive and compensate for temperature cross-sensitivity effects.

Main Results:

• The developed Fiber Bragg grating (FBG)-based sensor demonstrated high linearity for both torque and angle measurements.
• The sensor system exhibited a small error margin across the tested range of torque and angle variations.
• Temperature cross-sensitivity was successfully quantified and accounted for, enhancing measurement accuracy.

Conclusions:

• Fiber Bragg grating (FBG) sensors provide a viable and accurate method for monitoring torque and angle in exoskeleton applications.
• The developed FBG sensor system offers a robust, compact, and potentially multiplexed solution compared to traditional sensors.
• This technology holds significant potential for advancing human-robot interaction through improved actuator feedback.