International Journal of Intelligent Machines and Robotics (7 papers in press)
LENIENT COMPUTATION IN CONTROLLING THE NON LINEAR SYSTEM BASED ON ADAPTIVE ERROR OPTIMIZATION IN MICROGRID
by T. Yuvaraja, K. Ramya
Abstract: This manuscript describes the hybrid learning algorithm for training the error optimization in an MIMO Non-Linear System. The automated controller is designed using Lenient Computation technique with an Levenberg-Marquardt training algorithm. The designed controller is interfaced to an Microgrid which has Renewable energy sources like Solar, Wind, Fuel Cell, Smart battery as Input and the output power generated due to these sources can be utilized for various grid and atomized applications. The erudition capability and designing methodology of adaptive networks and sturdiness of PID controllers are described. Finally, the study illustrates an offline mode comparison of PID based ANFIS and Neural controllers in terms of settling time, steady state error and overshoot.
Keywords: Lenient Computation; PID; ANFIS PID; ANN ARX Model; Neural Network; Renewable Energy Sources; Microgrid.
Robust Active Vision Industrial CAD Parts Recognition System
by Tushar Jain, Meenu , H.K. Sardana
Abstract: automated assembly systems the machine parts identification is entirely different from simple object recognition; moreover the ability of human to differentiate between correct and no correct machine parts are better but it is a difficult task for a machine. In general with fast moving machine parts on conveyor manual defect detection by human inspectors are impractical also it is expensive, inaccurate, subjective , eye straining and other health issues to quality control inspectors . A computer vision based non-contact inspection technique is developed with image processing methods by considering these problems, for defect detection in industrial machine parts. The present work will help the industrial robot used in assembly process and industrial inspection systems. In this paper features based industrial object detection techniques are implemented in MATLAB to recognize the presence of the industrial CAD parts in the query image. In the end the actual industrial tool images are also used to show the accuracy and robustness of the proposed machine vision system for industrial manufacturing automation.
Keywords: Active Vision; Industrial CAD Parts; Image Processing; Parts Recognition; Feature Based Algorithms.
Towards a Secure and Automated Platform for Fingerprint Based Electronic Voting Machine
by Ifthekhar Ahammad, Obaedur Rahim Rizbhi, Pradip Lal Biswas, Sanjana Siraj, Md. Juwel Chowdhury, Md. Ashraful Islam
Abstract: Electronic voting machines (EVM) inherit the act of voting using electronic systems to cast and count votes. This paper deals with the design and development of an electronic voting machine using biometric fingerprint identification system in order to provide better performance, flexibility and economic advantages with higher level of security to the casting and voting system. The proposed finger print based EVM allows the voters to scan their fingerprint, which is then compared with the database. Upon completion of voter identification, voters are allowed to cast their votes and casted votes are updated immediately. The proposed electronic voting system is fast, efficient and fraud-free. It provides better security with biometric fingerprint system, makes the voting machine user friendly and reduces the cost to a minimum level.
Keywords: EVM; Fingerprint; Voting machine; Automatic; Biometrics.
Optimal Conceptual Design and Vision based Control of a Fruit Harvesting Robot
by Saran Kumar K, Shivaramashankaran P S, Rizwan Asif S, Karthick S, Palani I.A, Lad B.K, Abhijeet Patil, Pratik Patil, Harsh Sharma
Abstract: The main contribution of this paper is to develop a vision based control of a robotic arm for harvesting fruits. The camera fixed in the gripper pad enables to precisely locate the fruit and pluck it from the branch. Rigorous stability analysis is done to ensure the guaranteed performance of the closed loop system. The camera feedback locates the exact position of the fruit; this enables the controller to track a suitable and optimal path to reach the target by performing desirable transformations. The manipulator with 5 - DOF (RRPRR) is designed and optimized for formulating simple control strategies. The finger like built gripper is electrically actuated to provide the necessary force required in harvesting the fruit. Also an additional bellow kind of structure is specially designed and located below the gripper which helps to roll down the harvested fruit on to the storage container without damaging it. Numerical simulation analysis was carried out along with the design realization to justify the context. The advancement in the field of agrionics have also been a source of inspiration in designing agricultural robots.
Keywords: Robot Design; Vision based Control; Harvesting Robot; Dynamics; Trajectory tracking; Agrionics.
Digital implementation of a self-triggered control approach for a mechatronic platform: experimental results
by Carlos Santos, Felipe Espinosa, Marta Marron, Javier Echevarria, Cristina Losada, Daniel Pizarro
Abstract: This paper addresses the design and presents the experimental results of an aperiodic remote-controlled mechatronic plant using a miniDK2 electronic development board. We compare the classic periodic control solution with our self-triggered approach. The triggering mechanism consists on evaluating if the measurement error exceeds a predefined value. This measurement error is defined as the difference between the output signal of a model with and without a Sample-&-Hold that is activated only at the triggering instants. The minimum inter-execution time is the reference period and the maximum time is set by the designer, guaranteeing the stability of the closed loop system. At each new triggering instant, the remote controller receives a new measurement of the system and sends the actuation signal to the mechatronic plant.
Keywords: aperiodic digital control; self-triggered control; remote control; miniDK2 development board.
Dynamic Simulation of Serial Robots under Force Control
by Arun Dayal Udai, Subir Kumar Saha
Abstract: The advantages of using force control in industrial robots are well known. Study of such systems in virtual environment in the form of simulation is of great help as most of the force controlled task works in close contact with the environment. In this paper, we show how to simulate different force control algorithms of a typical serial robot used in industries before deciding to choose a suitable one for real implementation. Hence, a proper dynamic model of the robot is essential which should be able to emulate the real robot, particularly, if the robot moves at relatively higher speeds. This is done here using the concept of the Decoupled Natural Orthogonal Complement (DeNOC) matrices which is known to provide recursive forward dynamic algorithm that is not only efficient but also numerically stable. Such simulation of robots under force control will allow users to tune the control gains without stopping the real robot in production floor. Besides, such simulation can be used as an education tool as well to help beginners to explore various types of control algorithms and their performances. In addition, the framework for simulation proposed in this paper can work as a good testbench to test the performances of either a new control law or a different dynamic algorithm and like. As an illustration, the DeNOC based dynamics was substituted with MATLAB's SimMechanics which can also perform dynamic simulation. The comparison of the results validated the concept and correctness of the numerical simulations.
Keywords: Dynamic Simulation; Force Control; Serial Robot; DeNOC.
Robust Industrial Vision System for Mechanical Parts Recognition
by Tushar Jain, Meenu , H.K. Sardana
Abstract: Automated recognition of mechanical parts is a task in manufacturing that has been automated at a comparatively slow pace. Nearly all of the existing object recognition systems, with the exception of very few experimental systems have been designed to recognize a single object. In this paper, this problem is solved in a great manner so that the same process can handle different 2-D recognition applications. Color images are used during object recognition. The Fourier descriptor method has been adopted for recognition of mechanical parts. This method recognizes an object by extraction of features from an object image. The objects may be classified using Artificial Neural Network (ANN). For training and testing in either case, the features are extracted by presenting the object in different orientations. A feed forward neural network structure that learns the characteristics of the training data through the back-propagation learning algorithm is employed. The emphasis is put on the choice of network architecture and setting of different parameters. The study also considers the effects of various user-defined parameters and noting their effect on classification accuracy. The effect of orientation angle of the object and sample size on overall accuracy is also considered on the used classifier.
Keywords: Automated Recognition; Mechanical parts; Object recognition systems; Image Processing; Artificial Neural Network; Intelligent Machines; and Robotics.