The sense of touch is essential for reliable mapping between the environment and a robot which interacts physically with objects. Presumably, an artificial tactile skin would facilitate safe interaction of the robots with the environment. In this work, we present our color-coded tactile sensor, incorporating plastic optical fibers (POF), transparent silicone rubber and an off-the-shelf color camera. Processing electronics are placed away from the sensing surface to make the sensor robust to harsh environments. Contact localization is possible thanks to the lower number of light sources compared to the number of camera POFs. Classical machine learning techniques and a hierarchical classification scheme were used for contact localization. Specifically, we generated the mapping from stimulation to sensation of a robotic perception system using our sensor. We achieved a force sensing range up to 18 N with the force resolution of around 3.6 N and the spatial resolution of 8 mm. The color-coded tactile sensor is suitable for tactile exploration and might enable further innovations in robust tactile sensing.
Researchers embedded an optical tactile sensor into the multi-modal tactile sensing system of an underwater robot gripper @cite_25 . As in @cite_10 and Optoforce sensor , the sensing principle is based on the light reflection delivered via POFs. The POFs can be used as force sensing elements due to the stray light, which is considered as a drawback in telecommunications @cite_1 . The deformation of a POF increases the losses of the light propagated inside, as the attenuation coefficient increases. In additon, the elasto-optic metamaterial presented in @cite_2 can change its refractive index due to pure bending. Such POFs are fabricated by the chemical vapor deposition technique. Their design generally relies upon the phenomenon of optical interference @cite_0 .
@cite_1 @cite_0 @cite_2 @cite_10 @cite_25
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This paper presents a fiber optic based tactile array sensor that can be employed in magnetic resonance environments. In contrast to conventional sensing approaches, such as resistive or capacitive-based sensing methods, which strongly rely on the generation and transmission of electronics signals, here electromagnetically isolated optical fibers were utilized to develop the tactile array sensor. The individual sensing elements of the proposed sensor detect normal forces; fusing the information from the individual elements allows the perception of the shape of probed objects. Applied forces deform a micro-flexure inside each sensor tactel, displacing a miniature mirror which, in turn, modulates the light intensity introduced by a transmitting fiber connected to a light source at its proximal end. For each tactel, the light intensity is read by a receiving fiber connected directly to a 2-D vision sensor. Computer software, such as MATLAB, is used to process the images received by the vision sensor. The calibration process was conducted by relating the applied forces to the number of activated pixels for each image received from a receiving fiber. The proposed approach allows the concurrent acquisition of data from multiple tactile sensor elements using a vision sensor such as a standard video camera. Test results of force responses and shape detection have proven the viability of this sensing concept. Tactile sensing is an important perception mode for robots, but the existing tactile technologies have multiple limitations. What kind of tactile information robots need, and how to use the information, remain open questions. We believe a soft sensor surface and high-resolution sensing of geometry should be important components of a competent tactile sensor. In this paper, we discuss the development of a vision-based optical tactile sensor, GelSight. Unlike the traditional tactile sensors which measure contact force, GelSight basically measures geometry, with very high spatial resolution. The sensor has a contact surface of soft elastomer, and it directly measures its deformation, both vertical and lateral, which corresponds to the exact object shape and the tension on the contact surface. The contact force, and slip can be inferred from the sensor’s deformation as well. Particularly, we focus on the hardware and software that support GelSight’s application on robot hands. This paper reviews the development of GelSight, with the emphasis in the sensing principle and sensor design. We introduce the design of the sensor’s optical system, the algorithm for shape, force and slip measurement, and the hardware designs and fabrication of different sensor versions. We also show the experimental evaluation on the GelSight’s performance on geometry and force measurement. With the high-resolution measurement of shape and contact force, the sensor has successfully assisted multiple robotic tasks, including material perception or recognition and in-hand localization for robot manipulation. This paper addresses 6-DOF (degree-of-freedom) tactile localization, i.e., the pose estimation of tridimensional objects using tactile measurements. This estimation problem is fundamental for the operation of autonomous robots that are often required to manipulate and grasp objects whose pose is a priori unknown. The nature of tactile measurements, the strict time requirements for real-time operation, and the multimodality of the involved probability distributions pose remarkable challenges and call for advanced nonlinear filtering techniques. Following a Bayesian approach, this paper proposes a novel and effective algorithm, named memory unscented particle filter (MUPF), which solves 6-DOF localization recursively in real time by only exploiting contact point measurements. The MUPF combines a modified particle filter that incorporates a sliding memory of past measurements to better handle multimodal distributions, along with the unscented Kalman filter that moves the particles toward regions of the search space that are more likely with the measurements. The performance of the proposed MUPF algorithm has been assessed both in simulation and on a real robotic system equipped with tactile sensors (i.e., the iCub humanoid robot). The experiments show that the algorithm provides accurate and reliable localization even with a low number of particles and, hence, is compatible with real-time requirements. Tactile sensing is required for human-like control with robotic manipulators. Multimodality is an essential component for these tactile sensors, for robots to achieve both the perceptual accuracy required for precise control, as well as the robustness to maintain a stable grasp without causing damage to the object or the robot itself. In this study, we present a cheap, 3D-printed, compliant, dual-modal, optical tactile sensor that is capable of both high (temporal) speed sensing, analogous to pain reception in humans and high (spatial) resolution sensing, analogous to the sensing provided by Merkel cell complexes in the human fingertip. We apply three tasks for testing the sensing capabilities in both modes; first, a depth modulation task, requiring the robot to follow a target trajectory using the high-speed mode; second, a high-resolution perception task, where the sensor perceives angle and radial position relative to an object edge; and third, a tactile exploration task, where the robot uses the high-resolution mode to perceive an edge and subsequently follow the object contour. The robot is capable of modulating contact depth using the high-speed mode, high accuracy in the perception task, and accurate control using the high-resolution mode.