Self-Adaptive Perception of Object's Deformability with Multiple Deformation Attributes Utilizing Biomimetic Mechanoreceptors

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.

Advanced Stiffness Sensing through the Pincer Grasping of Soft Pneumatic Grippers

In this study, a comprehensive approach for sensing object stiffness through the pincer grasping of soft pneumatic grippers (SPGs) is presented. This study was inspired by the haptic sensing of human hands that allows us to perceive object properties through grasping. Many researchers have tried to imitate this capability in robotic grippers. The association between gripper performance and object reaction must be determined for this purpose. However, soft pneumatic actuators (SPA), the main components of SPGs, are extremely compliant. SPA compliance makes the determination of the association challenging. Methodologically, the connection between the behaviors of grasped objects and those of SPAs was clarified. A new concept of SPA modeling was then introduced. A method for stiffness sensing through SPG pincer grasping was developed based on this connection, and demonstrated on four samples. This method was validated through compression testing on the same samples. The results indicate that the proposed method yielded similar stiffness trends with slight deviations in compression testing. A main limitation in this study was the occlusion effect, which leads to dramatic deviations when grasped objects greatly deform. This is the first study to enable stiffness sensing and SPG grasping to be carried out in the same attempt. This study makes a major contribution to research on soft robotics by progressing the role of sensing for SPG grasping and object classification by offering an efficient method for acquiring another effective class of classification input. Ultimately, the proposed framework shows promise for future applications in inspecting and classifying visually indistinguishable objects.

BaroTac: Barometric Three-Axis Tactile Sensor with Slip Detection Capability

Tactile sensors for robotic applications enhance the performance of robotic end-effectors as they ca n provide tactile information to operate various tasks. In particular, tactile sensors can measure multi-axial force and detect slip can aid the end-effectors in grasping diverse objects in an unstructured environment. We propose BaroTac, which measures three-axial forces and detects slip with a barometric pressure sensor chip (BPSC) for robotic applications. A BPSC is an off-the-shelf commercial sensor that is inexpensive, easy to customize, robust, and simple to use. While a single BPSC-based tactile sensor can measure pressure, an array of BPSC-based tactile sensors can measure multi-axial force through the reactivity of each sensor and detect slip by observing high frequency due to slip vibration. We first experiment with defining the fundamental characteristics of a single-cell BPSC-based sensor to set the design parameters of our proposed sensor. Thereafter, we suggest the sensing method of BaroTac: calibration matrix for three-axis force measurement and discrete wavelet transform (DWT) for slip detection. Subsequently, we validate the three-axis force measuring ability and slip detectability of the fabricated multi-cell BPSC-based tactile sensor. The sensor measures three-axis force with low error (0.14, 0.18, and 0.3% in the X-, Y- and Z-axis, respectively) and discriminates slip in the high-frequency range (75-150 Hz). We finally show the practical applicability of BaroTac by installing them on the commercial robotic gripper and controlling the gripper to grasp common objects based on our sensor feedback.