Self‐Adaptive Perception of Object's Deformability with Multiple Deformation Attributes Utilizing Biomimetic Mechanoreceptors
Waner Lin, Ziya Wang, Yingtian Xu, Zhixian Hu, Wenyu Zhao, Zhihao Zhu, Zhenglong Sun, Guoxing Wang, Zhengchun Peng
- Year
- 2023
- Citations
- 18
- Access
- Open access
Abstract
The perception of object's deformability in unstructured interactions relies on both kinesthetic and cutaneous cues to adapt the uncertainties of an object. However, the existing tactile sensors cannot provide adequate cutaneous cues to self-adaptively estimate the material softness, especially in non-standard contact scenarios where the interacting object deviates from the assumption of an elastic half-infinite body. This paper proposes an innovative design of a tactile sensor that integrates the capabilities of two slow-adapting mechanoreceptors within a soft medium, allowing self-decoupled sensing of local pressure and strain at specific locations within the contact interface. By leveraging these localized cutaneous cues, the sensor can accurately and self-adaptively measure the material softness of an object, accommodating variations in thicknesses and applied forces. Furthermore, when combined with a kinesthetic cue from the robot, the sensor can enhance tactile expression by the synergy of two relevant deformation attributes, including material softness and compliance. It is demonstrated that the biomimetic fusion of tactile information can fully comprehend the deformability of an object, hence facilitating robotic decision-making and dexterous manipulation.
Keywords
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