Abdul Md Mazid

A Robotic Opto-tactile Sensor for Assessing Object Surface Texture

This paper presents the design construction and working principle of a newly developed opto-tactile sensor for object surface assessment in robotic applications. The sensor provides information about surface texture and this can be used to assist in quality assurance and object recognition tasks. Surface texture causes flexing of protrusions on the sensor surface which in turn rotates a small mirror within the sensor. This rotation is measured optically as a variation in light intensity transmitted between two optical fibers. A mathematical relationship between these light intensity changes and geometrical parameters of the surface texture has been developed. The sensor has been fabricated and tested on a number of different surface textures and preliminary results of sensor output for differing textures are presented. It is envisaged that the sensor will be integrated into a robot system or other intelligent machine to assist with object recognition and evaluation of surface texture

Opto-tactile sensor for surface texture pattern identification using support vector machine

Experimental application of a recently developed opto-tactile sensor in object surface texture pattern recognition using soft computational techniques has been successfully demonstrated in this article. Design and working principles of a number of optical type sensors have been illustrated and explained. Using the opto-tactile sensor multiple surface texture patterns of a number of objects like a carpet, stone, rough sheet metal, paper carton and a table surface have been captured and saved in MATLAB environment. The captured data have been adopted to soft computational techniques like support vector machine (SVM) technique, decision tree (DT) C4.5 algorithm, and naive Bayes (NB) algorithm for their learning. Testing with unknown surfaces using these techniques shows promising results at this stage and demonstrates its potential industrial use with further development. Results suggest that the methodology and procedures presented here are well suited for applications in intelligent robotic grasping.

A method to control grip force and slippage for robotic object grasping and manipulation

A grip force and slippage control for robotic object manipulation, based on mechanical friction is presented. This approach allows a load to be held reliably in the robot gripper without application of excessive forces or allowing uncontrolled slippage. It is a simple, robust and low cost solution, and could be used for applications where low cost integrated grip force and slippage control are needed. This solution could be customized to provide reliable grip force and slippage control for light, medium or heavy load holding applications for a variety of different objects.

An Efficient Control Configuration Development for a High-speed Robotic Palletizing System

This article presents a newly developed robotized palletizer control configuration consisting of a graphical application running on a human-machine interface (HMI) touch screens, a PLC program, and a servo system that executes palletizer reconfiguration and makes high-speed robotic palletizing possible. The servo system of the palletizer allows the PLC to reconfigure the palletizer automatically, based on the configuration data, and to control the product feed to the robots that execute the palletizing function. This allows the robots to handle several boxes at a time and therefore achieve high-speed palletizing. Significant downtime reduction during batch changes is achieved due to simplification of operator tasks, which makes the configuration and control method ideally suitable for production environments with frequent batch changes. Packaging and materials handling industries may be financially benefited using this configuration and control method. These include low palletizer downtime, high palletizer throughput, low training costs, no rework due to configuration errors and wider availability of potential operators.

Factors that influence reliable object manipulation

This paper presents various factors that influence robotic object manipulation. Some of these factors are inherent to the object and mostly unavoidable, while others are controllable by the grasping and slippage control system designer. Some factors, such as object texture are obviously inherent to the object and unavoidable for the purpose of object manipulation. However, other factors, such as the choice of mechanism control resolution depend mostly on the designer. It is anticipated that this information will be helpful to designers of robotic object grasping and slippage control systems, and will contribute to better design decisions.

Grasping force estimation detecting slip by tactile sensor adopting machine learning techniques

Adequate grasping force estimation and slip detection is a vital problem in wider applications of robots and manipulators in industries as well as in our everyday life. In this paper, a new methodology for slip detection during grasping by robot grippers/end-effectors using tactile sensor has been presented. During the object slippage, the tactile sensor in touch with the object surface travels along the peaks and valleys of surface texture of the object which creates vibratory motions in the tactile. A newly developed mathematical model is used to compute the scattered energy of vibrations, which contains parameters of surface texture geometry as well as trial grasping force, and other relevant parameters. Using the scattered energy of vibrations predicted by soft computing techniques, an attempt to instantly estimate the adequate grasping force has been reasonably successful. Surface texture data, for experimental estimation of grasping force, were collected from a huge number of machined specimens and were used to build four different machine learning estimation techniques. Experimental results using linear regression (LR), simple linear regression (SLR), pace regression (PR) and support vector machine (SVM) demonstrate a relatively better technique for industrial applications.

Computer Integrated Manufacturing education to Mechanical Engineering students: Teaching, research and practice

This paper details the research and development of Computer Integrated Design and Manufacturing at Islamic University of Technology (IUT), Bangladesh, a subsidiary organ of the Organization of the Islamic Conference (OIC). IUT is basically an educational and research institution offering undergraduate and postgraduate degrees in the field of Engineering and Technology. The objectives of Computer Integrated Manufacturing (CIM) education are to provide industry with a new generation of engineers having interdisciplinary skills necessary to deal with state of the art technology in designing, manufacturing, maintenance, selecting, and procuring manufacturing engineering systems. The research activities on CIM systems, the course coverage and laboratory facilities are discussed in the paper. The available programs are four-year Mechanical Engineering degree program specialising in production engineering, Masters Program, and PhD. There is a thirty-two-weeks research project at the undergraduate curricula of mechanical engineering. Areas of research topics include CAD (computer aided design), CAM (computer aided manufacturing), CAPP (computer aided process planning), CNC (computer numerical control) machine tools, DNC (direct numerical control machine tools), FMS (flexible manufacturing systems), ASRS (automated storage and retrieval systems), use of robotics and automated conveyance, computerized scheduling and production control, and a business system integrated by a common database in the Department of Mechanical and Chemical Engineering at IUT.

Grasping Force Estimation Recognizing Object Slippage by Tactile Data Using Neural Network

Hierarchical and wider applications of robots, manipulators, and pick and place machines are facing challenges in industrial environments due to their insufficient intelligence for appropriately recognizing objects for grasping and handling purposes. Since robots do not posses self-consciousness, estimation of adequate grasping force for individual objects by robots or manipulators is another challenge for wider applications of robots and manipulators. This article suggests a mathematical model, recently developed, for computation of scattered energy of vibrations sensed by the stylus during an object slippage in robot grippers. The model includes in it dynamic parameters like trial grasping force, object falling velocity, and geometry of object surface irregularities. It is envisaged that using the said mathematical model, with the help of robust decision making capabilities of artificial neural network (NN), a robot memory could be able to estimate appropriate/optimal grasping force for an object considering its physiomechanical properties. On the basis of above mentioned mathematical model, this article demonstrates an experimental methodology of estimating adequate grasping forces of an object by robot grippers using Backpropagation (BP) neural networks. Four different algorithms have been explored to experiment the optimal grasping force estimation.

The effective radius and resistance to slippage

This work reveals that parallel gripper flat-jaw configuration affects grasping effectiveness. An important finding is the fact that object grasp reliability is influenced significantly by grippers ability to develop high resistance to object rotation in the gripper. The concept of effective torque radius, which increases resistance to object rotation in the gripper, is presented here and can be extrapolated to other grasping devices and grasping strategies to improve their reliability and make them more effective. Grippers with full-jaw contact surface and those with discrete contact areas have been investigated using simple experimental setups. Essential mathematical models needed for analytical investigation, based on simple mechanics for full-jaw contact surfaces and discrete-jaw contact surfaces, are presented. These may be useful for gripper jaw design purposes.

Robotic grasping and Manipulation Controller Framework architecture redevelopment

This paper details the further improvements obtained by redesigning a previously offered Manipulation Controller Framework to provide support to an innovative, friction-based object slippage detection strategy employed by the robotic object manipulator. This upgraded Manipulation Controller Framework includes improved slippage detection functionality and a streamlined architecture designed to improve controller robustness, reliability and speed. Improvements include enhancements to object slippage detection strategy, the removal of the decision making module and integration of its functionality into the Motion Planner, and the stream-lining of the Motion Planner to improve its effectiveness. It is anticipated that this work will be useful to researchers developing integrated robot controller architectures and slippage control.