Sukarnur Che Abdullah

Two-Hand-Arm Manipulation Based on Tri-axial Tactile Data

Tactile sensing ability is important for social robots, which perform daily work instead of persons. The authors have developed a three-axis tactile sensor based on an optical measurement method. Since our optical three-axis tactile sensor can measure distributed tri-axial tactile data, a robot equipped with the tactile sensors can detect not only grasping force but also slippage from its hands. In this paper, the authors have two objectives: one of them is evaluation of the three-axis tactile sensor in actual robotic tasks; the other is to demonstrate effectiveness of tri-axial tactile data for motion control. To accomplish these objectives, the authors have developed a two-hand-arm robot equipped with three-axis tactile sensors. In the robot motion control, we implement a recurrent mechanism in which the next behavior is induced by the tactile data to make the robot accept intention embedded in the environment. Since this mechanism is based on the tactile data, it is easy to apply it to communication between the hand-arms to obtain the best timing for cooperative work. In a series of experiments, the two-hand-arm robot performed object transfer and assembling tasks. Experimental results show that this tri-axial tactile base programming works well because appropriate behavior is induced according to slippage direction.

Development of Optical Three-Axis Tactile Sensor and its Application to Robotic Hand for Dexterous Manipulation Tasks

This paper presents the development of a novel tactile sensor device called optical three-axis tactile sensor and its application to robotic hands. The proposed tactile sensor is based on optical waveguide transduction method combines with image processing technique. The hardware structure, sensing principles and force detection method are presented in conjunction with application to the robot hand control system. Since this tactile sensor can detect force from tri-axial force directions, not only normal and shear force distributions are acquired, but also slippage sensation is defined. The control parameters of robot hand system combined with the tactile sensor are defined from calibration tests. The performance of the tactile sensor that mounted to robot hand is verified in experiments of grasping, manipulating and twisting motions. Experimental results revealed that the robot hand system managed to recognize the tactile sensation in high accuracy during grasping, manipulating and twisting real objects.

All-in-type optical three-axis tactile sensor

The optical three-axis tactile sensor was developed to allow a robot to sense three-axis force distribution caused by touch. Although we have accomplished several manipulation tasks using a robot equipped with optical three-axis tactile sensors, the sensor has some problems related to compactness and insensible zone. The former problem is more serious for robotic manipulation and is caused by usage of external devices. In this study, we designed and developed an all-in-type three-axis tactile sensor, which includes all devices required for this sensor in its casing. Furthermore, this sensor features a rubber dome structure rather than the previous aluminum dome. Not only is this tactile sensor equipped with all devices on the inside, it also reduces the insensible zone and enlarges measurable range of force. In the design of the tactile sensor, we miniaturized its whole structure through adoption of a CMOS board camera equipped with LEDs. Since a USB is installed in the CMOS camera as an interface, an additional image processing board is not required. In evaluation experiments, although this sensor shows considerable non-linear characteristics, it possesses sufficient sensitivity and acceptable force range for both normal and tangential forces.

Robot arm simulation using 3D software application with 3D modeling, programming and simulation support

This paper present a new robotics simulator, completely based on Blender 3D and Python as scripting language. The simulation of robot works smoothly with six degree of freedom implemented within it, as the end effector starts to pick the object and to place it, all the links shown their rotation which definitely shown the movement mimics the real movement of robotic arm. The simulation was done with key frame animation and game engine simulation and comparison made relatively to the result obtained.

Multi geometrical image processing based on active vision agent

This paper presents an image processing capable of detecting basic shapes that communicate as intelligent agent by active vision sensor system. We also propose image recognition algorithm to facilitate a binocular camera to be able to detect and recognize multi geometrical object shapes. We investigate within plain and crowded background environment, indoor and outdoor along with low noise and very high noise that complicate the edge shape. The evaluation result shows plain environment give the highest recognition percentage followed by indoor and outdoor. Meanwhile the unsatisfied result is due to the high noises that make the gradient magnitude cannot distinguish between the multi geometrical edges and background because of their similar or shared pixels. However, the image processing efficiency is improved and able to distinguish image in real time navigation and amenable to spatial agent memory architectures. In near future, the result shall be used as a capstone for future application and development in humanoid robot sensor field.

Evaluation of hand direction for stroke patient based on 3R under-actuated robot manipulator

This paper presents a simplified version of hand exoskeleton design for stroke patient. The proposed powered exoskeleton concentrates at the hand of the human body. The simplified design is made using aluminum. The whole system is built with the cost in mind to make it more affordable as the current exoskeleton projects required heavy funding. Rotary Digital Encoder is used as sensors and the data from the sensors transferred out to the computer for keeping the position angle. The control is done using the MATLAB software. The 3R under-actuated robot manipulator consists of three links with three joining (First and Second joints as Active, attached motor and rotary digital encoder to theirself and Third joint as Passive, attach with rotary digital encoder and without motor to itself). A hand movement algorithm has been investigate by two methods; Simulation and Real-time. By using these methods, the hand algorithm has being evaluated in order to control the movement of passive joint according to the instruction during experiment.

The Structural Analysis of a Mini Hydroelectric System

In recent decades, renewable energy has been one of the major theme for researchers around the globe. In this research, one of the main objective is to verify on the structural reliability of the Mini Hydroelectric device. At first, the Engineering Design Process has been utilised in order to run the overall project efficiently, beginning from the conceptual stage until data analysis. As a result, the preliminary design has been accomplished within one year duration; with the support of 3D CAD design tool (Catia). Based from the simulation result, majority of the stress concentrates around the area between shaft and insert hole portion. The product (especially the inner parts) also able to withstand until 2 bar of water pressure, achieving minimum design safety factor of 1.3. Overall, the simulation result is always severe compared to theoretical calculation. This highlights the importance of using FEA simulation software such as ANSYS Workbench in validating the structural analysis result. In near future, by focusing on improving the performance of critical parts and areas; more opportunities especially in terms of design optimization is positively viewed.

Correction of image data using three-axis tactile sensing

The vision compensates for the limitations of tactile information and tactile sensing, and vice versa. The tactile sensor can obtain geometrical data as real scale, while image data requires calibration to obtain length as a metric unit. Even if stereovision is used, we cannot obtain sufficient precision. In the evaluation test, the robotic hand equipped with tactile sensors traces an object including convex and concave portions to evaluate edge trace precision. Error of distance obtained by the binocular vision is around ± 10 mm when distance between the camera and object is around 600 mm. When the hand-arm robot touches the convex portion of the object, size data obtained by the vision is modified within ± 0.5 mm accuracy. Since the robotic finger is too thick to touch the bottom of the concave, size data of the concave portion obtained by tactile sensing includes relatively large error of around 4 mm. However, the robot finger can follow the contour with ± 0.5 mm accuracy except for the bottom portion. Therefore, vision sensing is not sufficient for precise edge exploration and modification based on tactile sensing is required.

Grasping strategy of two robot arms based on tactile and slippage sensation of optical three-axis tactile sensor system

This paper presents a new grasping strategy of two robot arms based on active tactile and slippage sensation using a novel optical three-axis tactile sensor system. The tactile sensors are mounted on the tip of robotic hands of two robot arms. In the robot motion control, a recurrent mechanism was implemented in which the next behavior is induced by the tactile data to make the robot accept intention embedded in the environment. Since this mechanism is based on the tactile data, it is possible to apply it to communication between the hand-arms to obtain the best timing for cooperative work. A slippage distribution analysis was conducted which result shows that slippage occurred according to direction of force applied to the sensing elements. In experiments, the two-hand-arm robot performed object grasping, twisting, transferring and assembling tasks. Experimental results show that the proposed strategy has great potential to improve grasping performance of robot hand because appropriate behavior is induced according to tactile and slippage direction.

Object-Handling Tasks Based on Active Tactile and Slippage Sensations

Many tactile sensors have been developed to enhance robotic manufacturing tasks, such as assembly, disassembly, inspection and materials handling as described in several survey papers (Harmon, 1982; Nicholls & Lee 1989; Ohka, 2009a). In the last decade, progress has been made in tactile sensors by focusing on limited uses. Many examples of practical tactile sensors have gradually appeared. Using a Micro Electro Mechanical System, MEMS-based tactile sensors have been developed to incorporate pressure-sensing elements and piezoelectric ceramic actuators into a silicon tip for detecting not only pressure distribution but also the hardness of a target object (Hasegawa et al., 2004). Using PolyVinylidene DiFluoride, a PVDF film-based tactile sensor has been developed to measure the hardness of tumors based on comparison between the obtained sensor output and the input oscillation (Tanaka et al., 2003). A wireless tactile sensor using two-dimensional signal transmission has been developed to be stretched over a large sensing area (Chigusa et al., 2007). An advanced conductive rubber-type tactile sensor has been developed to be mounted on robotic fingers (Shimojo et al., 2004). Furthermore, image based tactile sensors have been developed using a charge-coupled device (CCD) and complementary metal oxide semiconductor (CMOS) cameras and image data processing, which are mature techniques (Ohka, 1995, 2004, 2005a, 2005b, Kamiyama et al., 2005). In particular, the three-axis tactile sensor that is categorized as an image based tactile sensor has attracted the greatest anticipation for improving manipulation because a robot must detect the distribution not only of normal force but also of slippage force applied to its finger surfaces (Ohka, 1995, 2004, 2005a, 2005b, 2008). In addition to our three-axis tactile sensors, there are several designs of multi-axis force cells based on such physical phenomena as magnetic effects (Hackwood et al., 1986), variations in electrical capacity (Novak, 1989; Hakozaki & Shinoda 2002), PVDF film (Yamada & Cutkosky, 1994), and a photointerrupter (Borovac et al., 1996). Our three-axis tactile sensor is based on the principle of an optical waveguide-type tactile sensor (Mott et al., 1984; Tanie et al., 1986; Nicholls et al., 1990; Kaneko et al., 1992; Maekawa et al., 1992), which is composed of an acrylic hemispherical dome, a light source, an array of rubber sensing elements, and a CCD camera (Ohka, 1995, 2004a, 2005a, 2005b, 2008). The sensing element of the silicone rubber comprises one columnar feeler and eight conical