International Journal of Robotics & Automation Jan - Jun 2016 IJRA www.journalspub.com
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Sciences, VIT University, Chennai (Tamil Nadu), India
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From the Editor's Desk
Dear Readers,
We would like to present, with great pleasure, the inaugural volume of a new scholarly
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emerging technologies.
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1. Simulation of the Inverse-Kinematics for JACO Manipulator Robot Arm Salih Rashid Majeed, Klause D-Kuhnert 1
2. Review on Improving Techniques for Path Planning Process in Robot Designing Himanshu Garg 7
3. Highlights on Trajectory Optimization of Robots
S. Sharmila 12
5. Passivity Analysis of Uncertain Stochastic Neural Networks with Discrete and Distributed Time-Varying DelaysM. Syed Ali, M. Esther Rani 19
4. A Brief Account on Dynamics of Robotic ArmPriyanka Garg 15
Contents
IJRA (2016) 1–6 © JournalsPub 2016. All Rights Reserved Page 1
International Journal of Robotics and Automation Vol. 2: Issue 1
www.journalspub.com
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 video-
game 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]
IJRA (2016) 7–11 © JournalsPub 2016. All Rights Reserved Page 7
International Journal of Robotics and Automation Vol. 2: Issue 1
www.journalspub.com
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
IJRA (2016) 12–14 © JournalsPub 2016. All Rights Reserved Page 12
International Journal of Robotics and Automation Vol. 2: Issue 1
www.journalspub.com
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
optimization. [1]
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
IJRA (2016) 15–18 © JournalsPub 2016. All Rights Reserved Page 15
International Journal of Robotics and Automation Vol. 2: Issue 1
www.journalspub.com
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
IJRA (2016) 19–32 © JournalsPub 2016. All Rights Reserved Page 19
International Journal of Robotics and Automation Vol. 2: Issue 1
www.journalspub.com
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
International Journal of
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and Design¬ International Journal of Manufacturing and
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« 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
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Applied Mechanics
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Computer Science and Engineering « International Journal of Wireless Network Security« International Journal of Algorithms Design and Analysis« International Journal of Mobile Computing Devices« International Journal of Software Computing and Testing« International Journal of Data Structures and Algorithms
Nanotechnology« International Journal of Applied Nanotechnology« International Journal of Nanomaterials and Nanostructures« International Journals of Nanobiotechnology
« International Journal of Solid State Materials« International Journal of Optical Sciences
Physics
« International Journal of Renewable Energy and itsCommercialization
« International Journal of Environmental Chemistry« International Journal of Agrochemistry« International Journal of Prevention and Control of Industrial
Pollution
Civil Engineering« International Journal of Water Resources Engineering« International Journal of Concrete Technology« International Journal of Structural Engineering and Analysis« International Journal of Construction Engineering and
Planning
Electrical Engineering« International Journal of Analog Integrated Circuits« International Journal of Automatic Control System« International Journal of Electrical Machines & Drives« International Journal of Electrical Communication
Engineering« International Journal of Integrated Electronics Systems and
Circuits
Material Sciences and Engineering « International Journal of Energetic Materials« International Journal of Bionics and Bio-Materials« International Journal of Ceramics and Ceramic Technology« International Journal of Bio-Materials and Biomedical
Engineering
Chemistry « International Journal of Photochemistry« International Journal of Analytical and Applied Chemistry« International Journal of Green Chemistry« International Journal of Chemical and Molecular
Engineering
« International Journal of Electro Mechanics andMechanical Behaviour
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Biomaterials« International Journal of Plant Biotechnology« International Journal of Molecular Biotechnology« International Journal of Biochemistry and Biomolecules« International Journal of Animal Biotechnology and
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