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International Journal of Robotics and Automation

International Journal of Robotics and Automation is designed with a special aim to strengthen the scientific communication among the world class industries and institutions that are working day and night to bring revolutionary changes in the field of automation. Journal largely covers all the man made and industrial development localized or geographically distributed. Automation system broadly includes industrial, factory or building automation, robotics, hydraulics and fields related to the automation technologies. Review articles, original research papers and other form of comprehensive scientific communications are encouraged.

Focus and Scope of the Journal !

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Industrial Automation Industrial Robotics Robotic Automation Factory Automation Building Automation Robotics Drives and Controls, Hydraulics and Pneumatics Sequential control and logical sequence control Manufacturing Automation Distributed control and remote monitoring System integration Fault detection and isolation (FDI) Virtual instrumentation and control Field-bus technology and interfaces

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PUBLICATION MANAGEMENT TEAM

Chairman Mr. Puneet Mehrotra Managing Director, JournalsPub, New Delhi

INTERNAL MEMBERS

Associate Manager Hidam Renubala

Commissioning Editors Ankita Singh

Priyanka Garg

Akanksha Marwah

Chhavi Goel

Deepika Bhadauria

Shrawani Verma

EDITORIAL BOARD MEMBERS Dr. Arindam Kumar Chanda Department of Mechanical & Automation Engineering, G. B. Pant Govt. Engineering College, Okhla, New Delhi, India

Dr. P. Narendra Mohan University College of Engineering and Technology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur (Andhra Pradesh), India

Dr. V. Sugumaran School of Mechanical Engineering and Building Sciences, VIT University, Chennai (Tamil Nadu), India

Dr. S Ramabalan Department of Mechanical Engineering, E. G. S. Pillay Engineering College, Nagapattinum (Tamil Nadu), India

Dr. Ajith Ramesh Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore (Tamil Nadu), India

Akhil Muralidharan Vellore Institute of Technology, Chennai (Tamil Nadu), India

From the Editor's Desk Dear Readers, We would like to present, with great pleasure, the inaugural volume of a new scholarly journal, International Journal of Robotics and Automation. This journal is part of the Engineering Sciences, and is devoted to the scope of present Robotics and Automation issues, from theoretical aspects to application-dependent studies and the validation of emerging technologies. This new journal was planned and established to represent the growing needs of Robotics and Automation as an emerging and increasingly vital field, now widely recognized as an integral part of scientific and technical investigations. Its mission is to become a voice of the Mechanical Engineers community, addressing researchers and practitioners in this area. The core vision of International Journal of Robotics and Automation in Journals Pub is to propagate novel awareness and know-how for the profit of mankind ranging from the academic and professional research societies to industry practitioners in a range of topics in Robotics and Automation in general. Journals Pub acts as a pathfinder for the scientific community to published their papers at excellently, well-time & successfully. International Journal of Robotics and Automation focuses on original high-quality research in the realm of Industrial automation, Factory automation, Building automation, Robotics, Hydraulics and pneumatics, Manufacturing automation, System integration etc. The Journal is intended as a forum for practitioners and researchers to share the techniques of Robotics and Automation and solutions in the area. Many scientists and researchers have contributed to the creation and the success of the Robotics and Automation community. We are very thankful to everybody within that community who supported the idea of creating an innovative platform. We are certain that this issue will be followed by many others, reporting new developments in the field of Robotics and Automation. This issue would not have been possible without the great support of the Editorial Board members, and we would like to express our sincere thanks to all of them. We would also like to express our gratitude to the editorial staff of JournalsPub, who supported us at every stage of the project. It is our hope that this fine collection of articles will be a valuable resource for Mechanical readers and will stimulate further research into the vibrant area of Robotics and Automation. Puneet Mehrotra Managing Director

Contents 1. Simulation of the Inverse-Kinematics for JACO Manipulator Robot Arm Salih Rashid Majeed, Klause D-Kuhnert

2. Review on Improving Techniques for Path Planning Process in Robot Designing Himanshu Garg

3. Highlights on Trajectory Optimization of Robots S. Sharmila

4. A Brief Account on Dynamics of Robotic Arm Priyanka Garg

5. Passivity Analysis of Uncertain Stochastic Neural Networks with Discrete and Distributed Time-Varying Delays M. Syed Ali, M. Esther Rani

International Journal of Robotics and Automation Vol. 2: Issue 1

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Simulation of the Inverse-Kinematics for JACO Manipulator Robot Arm Salih Rashid Majeed*, Klause D-Kuhnert Institute for Real Time Learning System, Siegen University, Germany

Abstract The inverse kinematics is a very important issue in the automation of robotic manipulator. This paper deals with inverse kinematics (IK) in the simulation process, using “JACO” robotic arm and studies about the implementation of IK in a very simple manner, using BLENDER software. In this program, we simulated IK based on the relationship between the bones and the skin in natural life, making use of some options included in the program. Keywords: inverse kinematics, BLENDER, JACO robot arm, Morse

INTRODUCTION The significance of inverse kinematics (KI) in the manipulator robotic arm due to direct effect in the movement of the arm, made the study of this relationship very important in the robot field. The use of robot arm in many applications persuades the necessity of studying the robot arm mechanism and discovers the easy methods to attain soft and real movement in the robot arm. Our algorithm subjects to one of the effective and well-known simulation programs for implementing this design and mechanism instead of the classical calculations applied before. RELATED WORK Monzani et al. represented a method to re target a motion from one character to a geometrically and topologically different one, and demonstrate how IK can conserve the motion-captured posture while enforcing Cartesian constraints. Their tool has been successfully applied to videogame projects. Extensions could be used to define more complex mapping functions, rather than one-to-one correspondences, to be able to handle coupled DOFs. [1]

Grochow et al. present an IK system based on a learned model of human poses given a set of constraints, our system can produce the most likely pose satisfying those constraints, in real time training, the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses which means that our IK system can generate any pose, but prefers to the poses that are most similar to the space of poses in the training data, they represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model and their style-based IK can replace conventional IK, wherever it is issued in computer animation and computer vision. [2] Der et al. stated that their IK algorithm provides intuitive and direct control of non-rigid shapes similar to a conventional IK algorithm for jointed rigid skeletons. It manages the geometric complexity of detailed meshes by using a reduced deformable model that parameterizes shape deformation with a compact set of control parameters. [3]

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International Journal of Robotics and Automation Vol. 2: Issue 1

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Review on Techniques for Path Planning Process in Robot Designing Himanshu Garg* H. P. University, Shimla, India

Abstract Path-planning is one of the basic and quite interesting issues in mobile robotic design since Mobile robot path planning has a few main properties according to type of environment, algorithm and completeness. The properties are whether it is static or dynamic, local or global and complete or heuristic. The static path planning refers to environment which contains no moving objects or obstacles other than a navigating robot and dynamic path planning refers to environment which contains dynamic moving and changing object such as moving obstacle. Meanwhile the local and global path planning depend on algorithm where the information about the environment is a priori or not to the algorithm. If the path planning is a global, information about the environment already known based of map, cells, grid or etc and if the path planning is a local, the robot has no information about the environment and robot has to sense the environment before decides to move for obstacle avoidance and generate trajectory planning toward target. The problems in path planning and its optimization is thus a popular research topic for many researchers across the globe, efforts are made in past to solve this problem and quite interesting results have been seen. In the present paper, a brief idea about the process of path planning in mobile robots with a short review of various contributions in improvising path planning process are presented. Keywords: path planning, optimization, algorithm, mobile robot, industrial robot, probabilistic roadmap (PRM), genetic algorithm (GA), degree of freedom (DOF).

INTRODUCTION Nowadays, no one can deny the importance of robotics in our daily life. It is a continuously expanding field because of its wide applications in agriculture, industry, home, surveillance, hospitals, etc. Robots can be roughly divided into two types based on their structure and usage, namely mobile robots and industrial robots. Mobile robots have the capability to move around in their environment and are not fixed to one physical location. In contrast, industrial robots usually consist of a jointed arm (multi-linked manipulator) and gripper assembly (or end effector) that is attached to a fixed surface. Mobile robots have a very diversified and broad area of application. [1] These robots are

used in navigation, surveillance, agriculture, cooperation, and in many other fields as it has a larger workspace compared to industrial robot. Coordinating the motion of multiple mobile robots is one of the fundamental problems in robotics. The predominant algorithms for coordinating teams of robots are decoupled and prioritized, thereby avoiding combinatorial hard planning problems typically faced by centralized approaches. While these methods are very efficient, they have two major drawbacks. First, they are incomplete, i.e. they sometimes fail to

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International Journal of Robotics and Automation Vol. 2: Issue 1

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Highlights on Trajectory Optimization of Robots S. Sharmila University of Madras, Tamil Nadu, India

INTRODUCTION Industrial robots are used as a primary means of contemporary automation due to their potential of productivity increase and product quality improvement. Industrial robots are widely used to achieve the demand of high precision and high speed positioning. Typical examples are welding, cutting, pick and place, and gluing. The productivity by industrial robots can further be maximized by path [1] optimization. Optimal motion can help in reducing the machine cycle and increasing the system utilization. In optimization, one determines the control signals that will derive the manipulator from starting point to ending point while minimizing the specified objective function, subject to the constraints on controlling signals and the position of the manipulator. Normally, the optimal control problem for robotic manipulators is complex because of the non-linearity and complex dynamics. [2] Trajectory optimization is a key problem in robotics, in particular when the time needed for optimization is critical. For instance, in interactive scenarios with a human, robots need to react on dynamic changes of the environment and the optimization time should be no more than the temporal duration of the movement itself so that an online sequencing is in principle possible. Marc Toussaint [2] proposed a probabilistic model for which the maximum likelihood (ML) trajectory coincides with the optimal trajectory and which, in the LQG case, reproduces the classical SOC solution. The algorithm then

utilizes approximate inference methods (similar to expectation propagation) that efficiently generalize to non-LQG systems. We demonstrate the algorithm on a simulated 39-DoF humanoid robot. [3] TRAJECTORY OPTIMIZATION Trajectory optimization is the second step of path optimization in which a predefined geometrical path, obtained by path planning step, is optimized for a desired objective. The importance of trajectory optimization is clearly obvious from the relationship among task execution, productivity and energy consumption. Research work on trajectory optimization started in late 1960s when Stepanenko discussed the minimum energy control problem for manipulators. [1] Kahn and Roth proposed the near time optimal control for open-loop articulated kinematic chains. The suboptimal solution was expressed in terms of switching curves for each of the system controls. The constraints for the optimization problem were the constant maximum/minimum forces. As this method finds the suboptimal solution, therefore, it was computationally quite efficient compare to the optimal control techniques. Luh et al. proposed the optimum path planning method using the “Method of Approximate Programming (MAP)” in which constraints are applied on Cartesian velocities and accelerations. [4] The Cartesian velocities and accelerations bounds were identified experimentally. This method was computationally expensive because the transformation from

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International Journal of Robotics and Automation Vol. 2: Issue 1

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A Brief Account on Dynamics of Robotic Arm Priyanka Garg University of Madras, Tamil Nadu, India

INTRODUCTION The purpose of control is to keep fixed or to alter the dynamical behavior of a physical system according to the wishes of man that can be formulated in words of performances and goals. The kinematic models of robots describe how the motion of the joints of a robot is related to the motion of the rigid bodies that make up the robot. [1] We implicitly assumed that we could command arbitrary joint level trajectories and that these trajectories would be faithfully executed by the real-world robot. Robotic dynamics zeros in on relationship between the forces acting on a robot mechanism and the accelerations they produce. Robotic Arm Taking a closer look at how to execute a given joint trajectory on a robot manipulator, we observe that most robot manipulators are driven by electric, hydraulic, or pneumatic actuators, which apply torques (or forces, in the case of linear actuators) at the joints of the robot. [3]

The dynamics of a robot manipulator describes how the robot moves in response to these actuator forces. For simplicity, we would need to assume that the actuators do not have dynamics of their own and, hence, we can command arbitrary torques at the joints of the robot. This allows us to study the inherent mechanics of robot manipulators without worrying about the details of how the joints are actuated on a

particular robot. Other problems in robot dynamics include:  Calculating the coefficients of the equation of motion.  Inertia parameter identification estimating the inertia parameters of a robot mechanism from measurements of its dynamic behavior. Ten inertia parameters are required to define the inertia of a single rigid body (mass, location of center of mass, and six rotational inertia parameters); so a dynamic model will normally contain 10 inertia parameters per body. However, when bodies are connected together to form a mechanism, they lose some of their degrees of motion freedom. [5] As a result, some of their inertia parameters may have no effect on the dynamic behavior of the system, or may be indistinguishable from algebraic combinations of other inertia parameters. Also, if the task is to obtain a dynamic model of a robot mechanism from a given kinematic model and a set of measurements of its dynamic behavior, then the procedure is to identify the values of a set of base inertial parameters, which are the set of observable inertia parameters of the system. [5] LITERATURE SURVEY There is a considerable range of literature on robotics research which exploits natural dynamics. This review divides the work into four categories: (i) research which exploits the robot kinematics and (i) statics, (iii) the dynamics of the robot

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International Journal of Robotics and Automation Vol. 2: Issue 1

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Passivity Analysis of Uncertain Stochastic Neural Networks with Discrete and Distributed Time-Varying Delays M. Syed Ali*, M. Esther Rani Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, India

Abstract This paper investigates the problem of robust passivity of uncertain stochastic neural networks with discrete and distributed time-varying delays. To reflect the most dynamical behaviors of the system, both parameter uncertainties and stochastic disturbance are considered, where parameter uncertainties enter into all the system matrices, stochastic disturbances are given in the form of a Brownian motion. By utilizing the Lyapunov functional method, the Itô differential rule and matrix analysis techniques, we establish sufficient criterion such that, for all admissible parameter uncertainties and stochastic disturbances, the stochastic neural networks is robustly passive in the sense of expectation. The delay - dependent stability condition is formulated, in which the restriction of the derivative of the time-varying delay should be less than 1 is removed. The derived criteria are expressed in terms of linear matrix inequalities (LMIs) that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results. Keywords: delayed neural networks, linear matrix inequality, lyapunov functional, parameter uncertainty, passivity

INTRODUCTION In recent years, neural networks have received considerable attention due to their extensive applications in solving some optimization problem, associative memory, classification of patterns, and other areas. Time delays are unavoidably present due to the finite switching speeds of the amplifiers and the inherent communication time of neurons, and its existence will affect the stability of a network by creating oscillatory and instability characteristics. [13, 14] Therefore, the stability analysis of neural networks with delays has recently received much attention. [1, 10, 19, 27, 20, 23, 25, 26, 28, 32, 40] When one models real nervous systems, stochastic disturbance and parameter uncertainties are unavoidable to be

considered. Because in real nervous system, synaptic transmission is a noisy process brought on by random fluctuation from the release of neurotransmitters, [17] and the connection weights of the neuron depend on certain resistance and capacitance values that include uncertainties. Therefore, it is of practical importance to study the stochastic effects on the stability of neural networks with parameter uncertainties, some results related to this problem have been published in the papers mentioned in the references. [2, 7, 16, 18, 36–39] In addition, parameter uncertainties can be often encountered in real systems as well as neural networks, due to the modelling inaccuracies and/or changes in the

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« « « «

¬ ¬ International Journal of Industrial Engineering

Manufacturing International Journal of Mechanical Dynamics and Analysis International Journal of Fracture and damage Mechanics International Journal of Structural Mechanics and Finite Elements

and Design ¬ International Journal of Manufacturing and

Materials Processing ¬ International Journal of Mechanical Handling and

Automation 5 more...

Electronics and Telecommunication « International Journal of Radio Frequency Design « International Journal of VLSI Design and Technology « International Journal of Embedded Systems and Emerging

Technologies « International Journal of Digital Electronics « International Journal of Digital Communication and Analog

Chemical Engineering « International Journal of Renewable Energy and its

Commercialization « International Journal of Environmental Chemistry « International Journal of Agrochemistry « International Journal of Prevention and Control of Industrial

Pollution

5 more...

Signals

1 more...

Civil Engineering

Architecture « International Journal of Housing and Human Settlement

« « « «

2 more...

Electrical Engineering

Computer Science and Engineering « « « « «

« « « «

Nursing « « « « «

Robotics & Automation Jan - Jun 2016

IJRA

Material Sciences and Engineering

« « « «

5 more...

Biotechnology « International Journal of Industrial Biotechnology and « « « «

International Journal of

Nanotechnology « International Journal of Applied Nanotechnology « International Journal of Nanomaterials and Nanostructures « International Journals of Nanobiotechnology

Chemistry

Physics « International Journal of Solid State Materials « International Journal of Optical Sciences