Shih Benjamin, Christianson Caleb, Gillespie Kyle, Lee Sebastian, Mayeda Jason, Huo Zhaoyuan, Tolley Michael T
Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States.
Department of Nanoengineering, University of California, San Diego, San Diego, CA, United States.
Front Robot AI. 2019 Apr 30;6:30. doi: 10.3389/frobt.2019.00030. eCollection 2019.
Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g., geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors work effectively for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve both their exteroceptive and interoceptive capabilities. To address this challenge, we designed sensors that can be co-fabricated with soft robot bodies using commercial 3D printers, without additional modification. We describe an approach to the design and fabrication of compliant, resistive soft sensors using a Connex3 Objet350 multimaterial printer and investigated an analytical comparison to sensors of similar geometries. The sensors consist of layers of commercial photopolymers with varying conductivities. We characterized the conductivity of TangoPlus, TangoBlackPlus, VeroClear, and Support705 materials under various conditions and demonstrate applications in which we can take advantage of these embedded sensors.
软机器人的传感器设计是一个具有挑战性的问题，因为对于其功能至关重要的设计参数范围很广（例如，几何形状、材料、驱动类型等）。虽然传统的刚性传感器在特定情况下对软机器人有效，但直接集成到软机器人身体中的传感器有助于提高其外部感知和内部感知能力。为应对这一挑战，我们设计了可以使用商用3D打印机与软机器人身体共同制造的传感器，无需额外修改。我们描述了一种使用Connex3 Objet350多材料打印机设计和制造柔顺、电阻式软传感器的方法，并研究了与类似几何形状传感器的分析比较。这些传感器由具有不同电导率的商用光聚合物层组成。我们表征了TangoPlus、TangoBlackPlus、VeroClear和Support705材料在各种条件下的电导率，并展示了可以利用这些嵌入式传感器的应用。
