Hanafiah Yussof

Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO

The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability. The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO. This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI). This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment. The system utilizes external cameras, besides the robots own visual system. Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot. This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session.

Humanoid robot NAO: Review of control and motion exploration

Humanoids; a most intriguing subject to behold by both the engineers and the world at large. With the introduction of humanoid robot NAO by Aldebaran-Robotics in 2008, a performant biped robot is now available and affordable for research laboratories and the mass market. In this paper, an exploration of current trends in control methods of biped walks, behavior interface tools for motion control for NAO and imminent findings in both research areas are discussed. Future directions are for researchers to devise a unique controller with low power consumption without compromising the robots speed and robustness.

Low force control scheme for object hardness distinction in robot manipulation based on tactile sensing

This paper presents an application of a low force interaction method in a control scheme of robot manipulation based on tactile sensing. Our aim is to develop an intelligent control system that can distinguish the hardness of unknown objects so that robotic fingers can effectively explore the objects surface without altering its physical properties or causing damage. Initially we developed a novel optical three-axis tactile sensor system based on an optical waveguide transduction method capable of acquiring normal and shearing forces. The sensors are mounted on the fingertips of the multi-fingered humanoid robot arm. We proposed a new control scheme applying low force interaction to distinguish the hardness of unknown objects in robot manipulation tasks based on tactile sensing. The scheme utilized new control parameters obtained by calibration experiments using hard and soft objects that enable robot fingers to precisely control grasp pressure and define the slippage sensation of the given object. Finally, verification experiments of the proposed control scheme using a humanoid robot arm were conducted whose results revealed that the fingers system managed to recognize the hardness of unknown objects and complied with sudden changes of the objects weight during object manipulation tasks.

Object exploration and manipulation using a robotic finger equipped with an optical three-axis tactile sensor

To evaluate our three-axis tactile sensor developed in preceding papers, a tactile sensor is mounted on a robotic finger with 3-degrees of freedom. We develop a dual computer system that possesses two computers to enhance processing speed: one is for tactile information processing and the other controls the robotic finger; these computers are connected to a local area network. Three kinds of experiments are performed to evaluate the robotic fingers basic abilities required for dexterous hands. First, the robotic hand touches and scans flat specimens to evaluate their surface condition. Second, it detects objects with parallelepiped and cylindrical contours. Finally, it manipulates a parallelepiped object put on a table by sliding it. Since the present robotic hand performed the above three tasks, we conclude that it is applicable to the dexterous hand in subsequent studies.

Hybrid-Actuated Finger Prosthesis with Tactile Sensing

Finger prostheses are devices developed to emulate the functionality of natural human fingers. On top of their aesthetic appearance in terms of shape, size and colour, such biomimetic devices require a high level of dexterity. They must be capable of gripping an object, and even manipulating it in the hand. This paper presents a biomimetic robotic finger actuated by a hybrid mechanism and integrated with a tactile sensor. The hybrid actuation mechanism comprises a DC micromotor and a Shape Memory Alloy (SMA) wire. A customized test rig has been developed to measure the force and stroke produced by the SMA wire. In parallel with the actuator development, experimental investigations have been conducted on Quantum Tunnelling Composite (QTC) and Pressure Conductive Rubber (PCR) towards the development of a tactile sensor for the finger. The viability of using these materials for tactile sensing has been determined. Such a hybrid actuation approach aided with tactile sensing capability enables a finger design as an integral part of a prosthetic hand for applications up to the transradial amputation level.

Obstacle Avoidance in Groping Locomotion of a Humanoid Robot

This paper describes the development of an autonomous obstacle-avoidance method that operates in conjunction with groping locomotion on the humanoid robot Bonten-Maru II. Present studies on groping locomotion consist of basic research in which humanoid robot recognizes its surroundings by touching and groping with its arm on the flat surface of a wall. The robot responds to the surroundings by performing corrections to its orientation and locomotion direction. During groping locomotion, however, the existence of obstacles within the correction area creates the possibility of collisions. The objective of this paper is to develop an autonomous method to avoid obstacles in the correction area by applying suitable algorithms to the humanoid robots control system. In order to recognize its surroundings, six-axis force sensors were attached to both robotic arms as end effectors for force control. The proposed algorithm refers to the rotation angle of the humanoid robots leg joints due to trajectory generation. The algorithm relates to the groping locomotion via the measured groping angle and motions of arms. Using Bonten-Maru II, groping experiments were conducted on a walls surface to obtain wall orientation data. By employing these data, the humanoid robot performed the proposed method autonomously to avoid an obstacle present in the correction area. Results indicate that the humanoid robot can recognize the existence of an obstacle and avoid it by generating suitable trajectories in its legs.

Experiments on Stochastic Resonance Toward Human Mimetic Tactile Data Processing

In the present research, human tactile stochastic resonance (SR) capable of enhancing sensitivity by superimposing proper noise upon undetectable weak signals is utilized to enhance the tactile processing method for social robotics. We develop an experimental apparatus composed of a piezoelectric actuator and its controller, and generate a step several microns high mixed with noise to perform a series of psychophysical experiments. Since psychophysical experiments are conducted based on the Parameter Estimation by Sequential Testing (PEST) method, we produce a PEST program that generates a stimuli sequence based on PEST. The experimental result shows that variation in the difference threshold (Difference Limen; DL) has a local minimum point in the relationship between DL and noise. Therefore, the tactile sensation’s just noticeable difference (JND) is decreased by appropriate external noise. Since JND denotes the scale divisions of sensation in the human mind, the present result shows that precise tactile sensations are enhanced by the appropriate external noise. Finally, we introduce a neural network model composed of nonlinear neurons with the bi-stable equilibrium condition to explain this result. Although original sensor data do not represent the morphology of the fine texture, the neural network model extracts the morphology and distinguishes the wave amplitude of the fine texture.

Trajectory generation of robotic fingers based on tri-axial tactile data for cap screwing task

In a previous paper, we developed a robotic finger equipped with optical three-axis tactile sensors, of which the sensing cell can separately detect normal and shearing forces. With appropriate precision, the robotic finger was able to perform three tasks: scanning flat specimens to obtain the friction coefficient, following the contour of objects, and manipulating a parallelepiped case put on a table by sliding it on the table. In the present study, designed as a follow-up to the above study, a robotic hand is composed of two robotic fingers. Not only tri-axial force distribution directly obtained from the tactile sensor but also the time derivative of the shearing force distribution are used for the hand control algorithm: if grasping force measured from normal force distribution is lower than a threshold, grasping force is increased; the time derivative is defined as slippage; if slippage arises, grasping force is enhanced to prevent fatal slippage between the finger and an object. In the verification test, the robotic hand screws a bottle cap to close it. Although input finger trajectories were a rectangular roughly decided to touch and screw the cap, a segment of the rectangular was changed from a straight line to a curved line to fit the cap contour. We concluded that higher order tactile information such as tri-axial tactile data can reduce the complexity of the control algorithm.

Development of an Optical Three-Axis Tactile Sensor for Object Handing Tasks in Humanoid Robot Navigation System

Summary. Autonomous navigation in walking robots requires that three main tasks be solved: self-localization, obstacle avoidance, and object handling. This report presents a development and application of an optical three-axis tactile sensor mounted on a robotic finger to perform object handling in a humanoid robot navigation system. Previously in this research, we proposed a basic humanoid robot navigation system called the groping locomotion method for a 21-dof humanoid robot, which is capable of defining self-localisation and obstacle avoidance. Recently, with the aim to determining physical properties and events through contact during object handling, we have been developing a novel optical three-axis tactile sensor capable of acquiring normal and shearing force. The tactile sensor system is combined with 3-dof robot finger system where the tactile sensor in mounted on the fingertip. Experiments were conducted using soft, hard, and spherical objects to evaluate the sensors performance. Experimental results reveal that the proposed optical three-axis tactile sensor system is capable of recognizing contact events and has the potential for application to humanoid robot hands for object handling purposes.

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.