970 research outputs found
Human Like Adaptation of Force and Impedance in Stable and Unstable Tasks
Abstract—This paper presents a novel human-like learning con-troller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, with-out requiring interaction force sensing. Index Terms—Feedforward force, human motor control, impedance, robotic control. I
A survey on uninhabited underwater vehicles (UUV)
ASME Early Career Technical Conference, ASME ECTC, October 2-3, 2009, Tuscaloosa, Alabama, USAThis work presents the initiation of our underwater robotics research which will be focused on underwater
vehicle-manipulator systems. Our aim is to build an underwater vehicle with a robotic manipulator which has a robust system and also can compensate itself under the influence of the hydrodynamic effects. In this paper, overview of the existing underwater vehicle systems, thruster designs, their dynamic models and control architectures are given. The purpose and results of the existing methods in underwater robotics are investigated
Nonlinear control for Two-Link flexible manipulator
Recently the use of robot manipulators has been increasing in many applications such as medical applications, automobile, construction, manufacturing, military, space, etc. However, current rigid manipulators have high inertia and use actuators with large energy consumption. Moreover, rigid manipulators are slow and have low payload-to arm-mass ratios because link deformation is not allowed. The main advantages of flexible manipulators over rigid manipulators are light in weight, higher speed of operation, larger workspace, smaller actuator, lower energy consumption and lower cost. However, there is no adequate closed-form solutions exist for flexible manipulators. This is mainly because flexible dynamics are modeled with partial differential equations, which give rise to infinite dimensional dynamical systems that are, in general, not possible to represent exactly or efficiently on a computer which makes modeling a challenging task. In addition, if flexibility nature wasn\u27t considered, there will be calculation errors in the calculated torque requirement for the motors and in the calculated position of the end-effecter. As for the control task, it is considered as a complex task since flexible manipulators are non-minimum phase system, under-actuated system and Multi-Input/Multi-Output (MIMO) nonlinear system. This thesis focuses on the development of dynamic formulation model and three control techniques aiming to achieve accurate position control and improving dynamic stability for Two-Link Flexible Manipulators (TLFMs). LQR controller is designed based on the linearized model of the TLFM; however, it is applied on both linearized and nonlinear models. In addition to LQR, Backstepping and Sliding mode controllers are designed as nonlinear control approaches and applied on both the nonlinear model of the TLFM and the physical system. The three developed control techniques are tested through simulation based on the developed dynamic formulation model using MATLAB/SIMULINK. Stability and performance analysis were conducted and tuned to obtain the best results. Then, the performance and stability results obtained through simulation are compared. Finally, the developed control techniques were implemented and analyzed on the 2-DOF Serial Flexible Link Robot experimental system from Quanser and the results are illustrated and compared with that obtained through simulation
Robot Manipulators
Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world
Benchmarking Cerebellar Control
Cerebellar models have long been advocated as viable models
for robot dynamics control. Building on an increasing insight
in and knowledge of the biological cerebellum, many models have been
greatly refined, of which some computational models have emerged
with useful properties with respect to robot dynamics control.
Looking at the application side, however, there is a totally different
picture. Not only is there not one robot on the market which uses
anything remotely connected with cerebellar control, but even in
research labs most testbeds for cerebellar models are restricted to
toy problems. Such applications hardly ever exceed the complexity of
a 2 DoF simulated robot arm; a task which is hardly representative for
the field of robotics, or relates to realistic applications.
In order to bring the amalgamation of the two fields forwards, we
advocate the use of a set of robotics benchmarks, on which existing
and new computational cerebellar models can be comparatively tested.
It is clear that the traditional approach to solve robotics dynamics
loses ground with the advancing complexity of robotic structures;
there is a desire for adaptive methods which can compete as traditional
control methods do for traditional robots.
In this paper we try to lay down the successes and problems in the
fields of cerebellar modelling as well as robot dynamics control.
By analyzing the common ground, a set of benchmarks is suggested
which may serve as typical robot applications for cerebellar models
Cartesian Parallel Manipulator Modeling, Control and Simulation
Ayssam Elkady, Galal Elkobrosy, Sarwat Hanna, and Tarek Sobh's book chapter on robotic parallel manipulators
Lungs cancer nodules detection from ct scan images with convolutional neural networks
Lungs cancer is a life-taking disease and is causing a problem around
the world for a long time. The only plausible solution for this type of disease is
the early detection of the disease because at preliminary stages it can be treated
or cured. With the recent medical advancements, Computerized Tomography
(CT) scan is the best technique out there to get the images of internal body
organs. Sometimes, even experienced doctors are not able to identify cancer just
by looking at the CT scan. During the past few years, a lot of research work is
devoted to achieve the task for lung cancer detection but they failed to achieve
accuracy. The main objective of this piece of this research was to find an
appropriate method for classification of nodules and non-nodules. For classification, the dataset was taken from Japanese Society of Radiological Technology
(JSRT) with 247 three-dimensional images. The images were preprocessed into
gray-scale images. The lung cancer detection model was built using Convolutional Neural Networks (CNN). The model was able to achieve an accuracy of
88% with lowest loss rate of 0.21% and was found better than other highly
complex methods for classification
Coordination control of robot manipulators using flat outputs
Published ArticleThis paper focuses on the synchronizing control of multiple interconnected flexible robotic manipulators
using differential flatness theory. The flatness theory has the advantage of simplifying trajectory tracking
tasks of complex mechanical systems. Using this theory, we propose a new synchronization scheme
whereby a formation of flatness based systems can be stabilized using their respective flat outputs.
Using the flat outputs, we eliminate the need for cross coupling laws and communication protocols
associated with such formations. The problem of robot coordination is reduced to synchronizing the
flat outputs between the respective robot manipulators. Furthermore, the selection of the flat output
used for the synchronizing control is not restricted as any system variable can be used. The problem of
unmeasured states used in the control is also solved by reconstructing the missing states using flatness
based interpolation. The proposed control law is less computationally intensive when compared to earlier
reported work as integration of the differential equations is not required. Simulations using a formation
of single link flexible joint robots are used to validate the proposed synchronizing control
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