Damith Suresh Chathuranga

Robust real time material classification algorithm using soft three axis tactile sensor: Evaluation of the algorithm

Materials and textures identification is a desired ability for robots. Developing such systems require tactile sensors that have enough sensitivity and spatial resolution, and the computational intelligence to meaningfully interpret sensor data. This paper introduces a texture classification algorithm utilizing support vector machine (SVM) classifier. Data taken from a novel three axis tactile sensor that utilize magnetic flux measurements for transduction was used to obtain the three dimensional tactile data. Frobenius norm calculated from the covariance matrix of the above data and the mean values of the three dimensional sensor data were used as features. Palpation velocity and small vertical load variances had minimum influence on the proposed algorithm. We have compared this algorithm with two other classification methods. They are: classify using the feature spatial period that is calculated from principal frequencies of the textures/material, and classify using neural network classifier with special properties of each materials tactile signals as features. For eight classes of material, the proposed algorithm performed faster and more accurately than the comparators when the scanning velocity and the vertical load varied.

Investigation of a biomimetic fingertip's ability to discriminate fabrics based on surface textures

Tactile sensing is an important ability for a humanoid robot which interacts in an unstructured environment. Such a system needs to sense and evaluate surface properties of objects that it interacts with. Among those properties, surface texture identification is a compulsory ability for certain kind of robot systems such as service robots, medical robots and exploratory robots. Therefore, the tactile system of above type of robots should have the ability to identify and discriminate textures with acceptable accuracy. A biomimetic fingertip that can be used in above kinds of robot tactile systems is introduced. The fingertip has the ability to detect force and vibration modalities. This paper reports the ability of the fingertip system to discriminate multiple materials (six fabrics and aluminium plate) by comparing the differences in their surface texture. The materials were classified using features: variance and power of the accelerometer signal. Moreover an Artificial Neural Network (ANN) classifier was evaluated by using the first 300 Fourier coefficients of the accelerometer signal as features. Above two methods used the raw signals of the accelerometers nearest to the contact area. Finally, an input signal was computed by calculating the covariance of two adjacent accelerometers. This new signal was used to calculate features for the ANN classifier. The results showed that the use of a convoluted signal improved the success rate of discriminating the seven textures.

A biomimetic soft fingertip applicable to haptic feedback systems for texture identification

Humans recognize textures using the tactile data obtained from the human somatosensory system. Recognition of textures allows humans discriminate objects and materials. Moreover, by understanding the objects or materials texture, the human intuitively estimates roughness and the friction properties of the object or the material. This ability is necessary for object manipulative tasks. Likewise artificial haptic systems too, should have the ability to encode textures and feedback those data to haptic applications such as haptic displays. In this paper a biomimetic soft fingertip sensor that can be used in above haptic systems is introduced. The fingertip has the ability to detect force and vibration modalities. We propose three features calculated from the covariance signal of two adjacent accelerometers in the fingertip to use in texture identification. The covariance signal is transformed using Discrete Wavelet Transform (DWT) and the three features mentioned below are calculated. The mean and variance of the approximate signal, and the energies of the detailed signal are chosen as features. Then, the proposed features were validate by using those in an Artificial Neural Network (ANN) to classify seven wood samples. The results showed a 65% success rate in classifying wood samples and that the proposed features are acceptable to encode textures.

Embedded electro-conductive yarn for shape sensing of soft robotic manipulators.

Flexible soft and stiffness-controllable surgical manipulators enhance the manoeuvrability of surgical tools during Minimally Invasive Surgery (MIS), as opposed to conventional rigid laparoscopic instruments. These flexible and soft robotic systems allow bending around organs, navigating through complex anatomical pathways inside the human body and interacting inherently safe with its soft environment. Shape sensing in such systems is a challenge and one essential requirement for precise position feedback control of soft robots. This paper builds on our previous work integrating multiple optical fibres into a soft manipulator to estimate the robots pose using light intensity modulation. Here, we present an enhanced version of our embedded bending/shape sensor based on electro-conductive yarn. The new system is miniaturised and able to measure bending behaviour as well as elongation. The integrated yarn material is helically wrapped around an elastic strap and protected inside a 1.5mm outer-diameter stretchable pipe. Three of these resulting stretch sensors are integrated in the periphery of a pneumatically actuated soft manipulator for direct measurement of the actuation chamber lengths. The capability of the sensing system in measuring the bending curvature and elongation of the arm is evaluated.

Image-Based Optical Miniaturized Three-Axis Force Sensor for Cardiac Catheterization

In order to determine the cause of and to treat an abnormal heart rhythm, electrophysiological studies and ablation procedures of the heart sensorized catheters are required. During catheterization, force sensors at the tip of the catheter are essential to provide quantitative information on the interacting force between the catheter tip and the heart tissue. In this paper, we are proposing a small sized, robust, and low-cost three-axis force sensor for the catheter tip. The miniaturized force sensor uses the fiber-optic technology (small sized multi-cores optical fiber and a CCD camera based on image processing to read out the forces by measuring light intensity, which are modulated as a function of the applied force. In addition, image processing techniques and a Kalman filter are used to reduce the noise of the light intensity signals. In this paper, we explain the design and fabrication of our three-axis force sensor and our approach for reducing noise levels by applying a Kalman filter model, and finally discuss the calibration procedure. Moreover, we provide an assessment of the performance of the proposed sensor.

Magnetic and Mechanical Modeling of a Soft Three-Axis Force Sensor

This paper describes the modeling of a soft three-axis force sensor. The sensor has a cylindrical cantilever beam made of silicone rubber that compresses and bends when normal and tangential forces are applied. The displacement of the beams end is calculated by measuring the change of the magnetic field emitted by a permanent magnet embedded in the soft beam, at fixed points in space. Spring theory and bending theory are used to calculate the normal and tangential force components. The normal forces calculated by the proposed model and the measured values have an error less than 5% validating the analogy of the sensor to a soft cantilever beam under compression and bending. The proposed mathematical model is simple and faster than a finite-element model, and accurately represents the non-linear behavior of the sensors physical effects to applied loads.

Disposable soft 3 axis force sensor for biomedical applications

This paper proposes a new disposable soft 3D force sensor that can be used to calculate either force or displacement and vibrations. It uses three Hall Effect sensors orthogonally placed around a cylindrical beam made of silicon rubber. A niobium permanent magnet is inside the silicon. When a force is applied to the end of the cylinder, it is compressed and bent to the opposite side of the force displacing the magnet. This displacement causes change in the magnetic flux around the ratiomatric linear sensors (Hall Effect sensors). By analysing these changes, we calculate the force or displacement in three directions using a lookup table. This sensor can be used in minimal invasive surgery and haptic feedback applications. The cheap construction, bio-compatibility and ease of miniaturization are few advantages of this sensor. The sensor design, and its characterization are presented in this work.

3D printed soft gripper for automatic lunch box packing

In this paper, we proposed a 3D printed soft robot gripper with modular design for lunch box packing. The gripper consists of a rigid base and three soft fingers. A snap-lock mechanism was designed for easy attach-detach assembly of the gripper without using screws. All components were 3D printed and the soft finger structure is based on the principle of fluidic elastomer actuator. Three finger designs, and soft gripper grasping and lifting deformable objects were investigated through finite element (FE) analysis and experiments. Results suggested that different finger designs yielded different curvature along the finger and generated different stress distribution once pressurized. The proposed gripper could grasp and lift objects with variable shapes and softnesses.

Multi-Axis Force/Torque Sensor Based on Simply-Supported Beam and Optoelectronics.

This paper presents a multi-axis force/torque sensor based on simply-supported beam and optoelectronic technology. The sensor’s main advantages are: (1) Low power consumption; (2) low-level noise in comparison with conventional methods of force sensing (e.g., using strain gauges); (3) the ability to be embedded into different mechanical structures; (4) miniaturisation; (5) simple manufacture and customisation to fit a wide-range of robot systems; and (6) low-cost fabrication and assembly of sensor structure. For these reasons, the proposed multi-axis force/torque sensor can be used in a wide range of application areas including medical robotics, manufacturing, and areas involving human–robot interaction. This paper shows the application of our concept of a force/torque sensor to flexible continuum manipulators: A cylindrical MIS (Minimally Invasive Surgery) robot, and includes its design, fabrication, and evaluation tests.

A soft three axis force sensor useful for robot grippers

A novel three axis force sensor, based on magnetic flux measurements, was used in the fingers of a gripper. The force sensor uses three Hall Effect sensors orthogonally placed at the base of a hemisphere made of silicon rubber. A neodymium permanent magnet was inside the hemisphere. When a force was applied to the perimeter of hemisphere, it compressed the hemisphere displacing the magnet. This displacement caused change in the magnetic field around the Hall-effect sensors. By analysing these changes, we calculated the force in three directions using a lookup table. This sensor can be used in robot grippers to manipulate objects dexterously with tactile feedback. The cheap construction, robustness and reliability are few advantages of this sensor for it to be used in industrial applications. The sensor design, simulation and its characterization are presented in this work. Furthermore, as an application, a peg in a hole experiment was carried out to present the ability of the sensors to be used in robot grippers for manipulation tasks.