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The need to create more viable soft sensors is increasing in tandem with the growing interest in soft robots. Several sensing methods, like capacitive stretch sensing and intrinsic capacitive self-sensing, have proven to be useful when controlling soft electro-hydraulic actuators, but are still problematic. This is due to challenges around high-voltage electronic interference or the inability to accurately sense the actuator at higher actuation frequencies. These issues are compounded when trying to sense and control the movement of a multiactuator system. To address these shortcomings, we describe a two-part magnetic sensing mechanism to measure the changes in displacement of an electro-hydraulic (HASEL) actuator. Our magnetic sensing mechanism can achieve high accuracy and precision for the HASEL actuator displacement range, and accurately tracks motion at actuation frequencies up to 30 Hz, while being robust to changes in ambient temperature and relative humidity. The high accuracy of the magnetic sensing mechanism is also further emphasized in the gripper demonstration. Using this sensing mechanism, we can detect submillimeter difference in the diameters of three tomatoes. Finally, we successfully perform closed-loop control of one folded HASEL actuator using the sensor, which is then scaled into a deformable tilting platform of six units (one HASEL actuator and one sensor) that control a desired end effector position in 3D space. This work demonstrates the first instance of sensing electro-hydraulic deformation using a magnetic sensing mechanism. The ability to more accurately and precisely sense and control HASEL actuators and similar soft actuators is necessary to improve the abilities of soft, robotic platforms.
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MechSense: A Design and Fabrication Pipeline for Integrating Rotary Encoders into 3D Printed Mechanisms
We introduce MechSense, 3D-printed rotary encoders that can be fabricated in one pass alongside rotational mechanisms, and report on their angular position, direction of rotation, and speed. MechSense encoders utilize capacitive sensing by integrating a floating capacitor into the rotating element and three capacitive sensor patches in the stationary part of the mechanism. Unlike existing rotary encoders, MechSense does not require manual assembly but can be seamlessly integrated during design and fabrication. Our MechSense editor allows users to integrate the encoder with a rotating mechanism and exports files for 3D-printing. We contribute a sensor topology and a computational model that can compensate for print deviations. Our technical evaluation shows that MechSense can detect the angular position (mean error: 1.4°) across multiple prints and rotations, different spacing between sensor patches, and different sizes of sensors. We demonstrate MechSense through three application examples on 3D-printed tools, tangible UIs, and gearboxes.
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- Award ID(s):
- 1955697
- PAR ID:
- 10480332
- Publisher / Repository:
- Association for Computing Machinery
- Date Published:
- Journal Name:
- CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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