A vision-based tactile sensor

In recent years, many tactile sensors have been developed with the advancement in robotics. For example, there are sensors that measure the contact state or force distribution. They are very useful, but the resolution of the measurement is still inferior as compared to that of a human. Thus, we propose a new type of optical tactile sensor that can detect surface deformation with high precision by using the principle of optical lever. We construct a tactile sensor that utilizes the resolution of a camera to the maximum by using transparent silicone rubber as a deformable mirror surface and taking advantage of the reflection image.

IEEE Transactions on Instrumentation and Measurement, 1994

The paper discusses a tactile sensor with a specially designed compliant overlay which reduces the tactile sensor distortions caused by deformations of the elastic material during probing. The sensor is used for model-based active tactile recognition of fixed 3-D objects. T to enhance the visual capability of robots by improving their ability to identify and measure the position and orientation of objects, especially during robotic manipulation operations [ 11-[4]. A robotic tactile-sensing system for object recognition and manipulation, in essence, emulates the mechanisms of human tactile perception. This is a complex process with two distinct modes: passive touch , produced by the "cutaneous" field of "mechanoreceptive" transducers covered by an elastic material which provides contact force, contact geometric profile, and temperature information, and active tactile sensing, which integrates cutaneous sensory data and "kinesthetic" information (i.e., limb/joint positions and the states of motor muscles) [4] and [ 5 ] . This paper discusses a tactile sensor system with high sampling resolution and its application for the active perception of stationary polyhedral objects which are larger than the tactile probe dimensions. The experimental tactile sensor is based on force-sensitive transducer technology and has an elastic overlay with protruding tabs which provides a de facto spatial sampling. The new elastic overlay design allows for a higher overall sampling resolution than other tactile sensor arrays which use one-piece elastic overlays [SI-[ 111. A tactile sensor is a contact-type measuring instrument which requires an extemal bias force to impose the contact with the touched object. Under the pressure of this force, the local geometric profile of the object's surface indents an elastic Manuscript

Robotics, IEEE Transactions …

Tactile information is valuable in determining properties of objects that are inaccessible from visual perception. In this work, we present a tactile perception strategy that allows a mobile robot with tactile sensors in its gripper to measure a generic set of tactile features while manipulating an object. We propose a switching velocity-force controller that grasps an object safely and reveals at the same time its deformation properties. By gently rolling the object, the robot can extract additional information about the contents of the object. As an application, we show that a robot can use these features to distinguish the internal state of bottles and cans -purely from tactile sensing -from a small training set. The robot can distinguish open from closed bottles and cans, and full ones from empty ones. We also show how the high-frequency component in tactile information can be used to detect movement inside a container, e.g., in order to detect the presence of liquid. To prove that this is a hard recognition problem, we also conducted a comparative study with 17 human test subjects. The recognition rates of the human subjects were comparable to that of the robot.

Smart Sensors and Sensing …, 2008

The surface recognition algorithm that determines the types of contact surfaces by fusing information collected by the tactile sensor system are proposed. This algorithm can recognize 3-D objects using a 2-fingered robot hand, on which tactile sensors are mounted. Experiments demonstrate the reliability of the surface classification method and the accuracy of transformations independent of object shape, translation and rotation. Surface recognition is a more complicated task in tactile perception than in visual perception. This is because there are a number of additional factors which affect the quality of tactile images such as complex strain-stress relationships in elastic overlays, amount of force, and contact angle during the tactile perception process.

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 2021

In this paper, a cyber-physical system composed by a tactile sensor, a robotic gripper and suitable ROS software nodes is proposed. The tactile sensors are shown to be compatible with three different commercial grippers, and the developed ROS nodes for the data acquisition and elaboration enable the implementation of complex tasks such as the grasping and the shape reconstruction of deformable linear objects like cables. The effectiveness of the systems is tested with cable of different diameters and with wiring harnesses composed by several cables grouped together, focusing on the reconstruction of linear and quadratic curves representing the cable shape. Experimental trials are also executed to show the possibility of exploiting the shape reconstruction provided by the proposed system to correct the gripper grasping pose.

Proceedings of International Conference on Robotics and Automation

Most robotic hands are either sensorless or lack the ability to accurately and robustly report position and force information relating to contact. This paper describes a robotic hand system that uses a limited set of native joint position and force sensing along with custom designed tactile sensors and real-time vision modules to accurately compute finger contacts and applied forces for grasping tasks. Three experiments are described: integration of real-time visual trackers in conjunction with internal strain gauge sensing to correctly localize and compute finger forces, determination of contact points on the inner and outer links of a finger through tactile sensing and visual sensing, and determination of vertical displacement by tactile sensing for a grasping task. contact forces. The Barrett hand has a limited amount of internal strain gauge force sensing capability built into it, and the tactile system can be used to accurately quantify contact forces in conjunction with the strain gauge system. Vision can be an effective sensing modality for grasping tasks due to its speed, low cost, and flexibility. It can serve as an external sensor that can provide control information for devices that lack internal sensing or that would require extensive modification and re-engineering to provide contact and force sensing. Using a vision system, a simple uninstrumented gripper/hand can become a precision device capable of position and possibly even force control. Additionally, when vision is coupled with any existing internal hand sensing, it can provide a rich set of complementary information to confirm and quantify internal sensory data, as well as monitoring a task's progress.

2016

Adapting the principle of natural vibrissae, artificial tactile sensors are designed to fulfill the functions: object distance detection, object shape recognition and surface texture scanning. To realize the process of surface texture detection with an artificial sensor, firstly a theoretical approach is done. Replacing the natural vibrissa by an Euler-Bernoulli bending beam and modeling the vibrissa-surface contact with respect to Coulomb’s Law of Friction, a quasi-static scenario is performed. In this, the support of the vibrissa moves in a way that the tip of the beam gets pushed. Starting the movement of the support, the tip of the beam is sticking to the surface until the maximal stiction force is reached. It follows a period of sliding and after this a period of stiction again. In dependence on the shape of the beam, the relation between the quasi-static movement and the present coefficient of static friction is analyzed. Keywords–Surface detection; vibrissae; friction; mechan...

Both tactile and visual sensing are important to solve problems of uncertainties inherent to the grasping tasks. In this paper, a hybrid approach based on combining tactile and RGBD data is proposed for control the robotic manipulation of elastic objects. The proposed method detects both interaction and lack of contact among fingertips and object. Also, this approach measures deformations to determine appropriated finger movements when the object's geometry changes in real time as a result of the deformation caused by the pressure. This approach was evaluated on a multi-fingered robot hand with tactile sensors in real experiments.

IEEE Transactions on Instrumentation and Measurement, 2002

A novel smart tactile sensor that recognizes the nature of the surfaces is presented. The approach is based on the idea of analyzing the signal produced when the sensor touches and stimulates the surface. An "intelligent probing" system for material recognition has been developed. It is based on the use of bimorph piezo-ceramic actuators and sensors that allow the unknown surface to be stimulated and the response signal sensed. Two different experimental prototypes of the tactile sensing system have been realized and their performance has been characterized. Several interesting applications have been considered with particular emphasis on the problems of "humanitarian demining" and automatic waste material recycling. Experimental results are given to show the efficiency of the smart measuring system. ).

This paper proposes an optical-based tactile sensor design concept, which uses a light angle and intensity sensor to infer force and displacement from deformations of a silicone pillar. The proposed design uses a simple, low?cost fabrication method with an overall small-scale form factor. The sensor can measure 3D displacement, 3D force, and vibration. The overall displacement estimation error (mean ± SD) in the X, Y, and Z axes was 40.2 µm ± 34.8 µm, 4.0 µm ± 55.3 µm and 13.3 µm ± 11.8 µm, respectively, over a full-scale lateral displacement of 1 mm radius in X and Y and 2.2 mm compression in Z. The overall force estimation error (mean ± SD) was 38.3 ± 29.6 mN, 40.1 ± 29.4 mN and 0.074 ±61.9 mN for a full-scale force of approximately 2 N in X or Y, and 6 N in Z. Sensitivity to vibrations in the range of 10-950 Hz was also evaluated showing good sensitivity over this entire range. This new sensing approach could be of benefit in robotic manipulation applications, as it could be easi...