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| About site: Robotics/Research - UNH Robotics Laboratory |
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| About site: http://www.ece.unh.edu/robots/rbt_home.htm |
Title: Robotics/Research - UNH Robotics Laboratory Researches the problem of vibration control in robotics through real-time control experiments and simulation.
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Robotics /Vib Cntrl HOME
Robotics / Vibration Control @ UNH
ECE
Home
 8
Faculty:
email:
Dr. L. Gordon Kraft
(gordon.kraft@unh.edu)
The research emphasis of the Robotics Laboratory in the Department of Electrical and Computer Engineering is
the application of fast associative memories and other neural network learning techniques
(such as CMAC neural networks) to problems in control, pattern
recognition, and signal processing. The basic concept is to design hardware/software
systems which improve their own performance through practice. Details of specific research
can be found in published papers and graduate student theses.
The Robotics Laboratory currently maintains six experimental
settings for research in learning control. The first includes a General Electric P-5 five
axis articulated industrial robotic arm which has been the basis of much of our real time
experimentation. This arm has been used both for studies of learning high speed dynamics
and of learning low speed hand-eye coordination (using video feedback). The second
experimental preparation includes two Scorbot-ER V table top robotic manipulators for use
in experiments involving path planning, multiarm cooperation and workspace obstacle
avoidance. The experiment includes a true binocular vision system which can be positioned
and oriented actively using a third table top robotic arm with six degrees of freedom. The
third major experimental preparation includes a ten degree of freedom biped walking
structure, with force sensing feet and a two-axis accelerometer for sensing balance. The
fourth experimental preparation includes a twenty degree of freedom quadruped walking
structure, also with force sensing feet and a two-axis accelerometer for sensing balance.
The fifth experiment involves a wheeled mobile robot with an array of ultrasonic range
finders for studies of adaptive navigation and map building. Finally, the six experiment
involves using neural network learning in the myoelectric control path of a Liberty
Technology Boston Elbow.
Computing in the laboratory is performed primarily using several
80486, Pentium and Pentium-Pro (P6) based engineering workstations, two massively
parallel SIMD processors, INMOS transputer based multi-processing systems, and special
purpose neural network hardware (developed at UNH). These systems support real time
control experiments, simulation studies, general purpose graphics and document
preparation. The laboratory also maintains equipment and tools for electronic hardware
development and testing (oscilloscopes, signal generators, power supplies, etc.).
Vibration
Control
by Gordon Kraft
Nearly everyone has experienced the annoyance of a long drive
with an unbalanced tire, or the whir of a noisy hard drive, or seen the blur in a picture
taken from a camera that moved as the shutter closed. If you saw the movie "Hunt for
Red October", you know how important submarine underwater vibrations are to the Navy.
The Hubbell telescope cannot function if the supporting platform in space is moving.
Factory workers are less efficient if they feel machinery vibrations for long periods of
time. All of these are examples of unwanted vibrations. Control of these unwanted
vibrations is a very important problem.
Most vibration control systems are passive. The rubber in your
car engine mounts or in air conditioning ducts are examples. These are called vibration
absorbers and by far most vibration reduction systems use these passive elements. In some
systems it's important to reduce the vibrations beyond the capability of the passive
systems. In these cases, an active feedback control loop is required. Simply stated, the
vibration measurements taken from various types of acceleration sensors are processed and
then applied to dynamic actuators to apply forces that oppose the vibrations. These
systems are usually subject to "ad hoc" adjustments to tune the feedback
controller for the particular application. Once the system is tuned the performance of the
system remains fixed. That is, it never gets any better as time goes on.
In 1997 we received an NSF grant for $365,000.00 to apply a
neural network called CMAC to the area of vibration control. The NSF grant is from the
Knowledge Modeling and Computational Intelligence Division of NSF (director is Dr. Paul
Werbos). The UNH Robotics Lab version of CMAC has been very successful at other types of
control systems such as robotics and signal processing applications. It has advantages
over other neural networks such as reduced memory requirements, faster training times and
faster real-time control cycles. The key point is that, with CMAC in the control loop, the
control system performance will continue to improve with time. As the network accumulates
more experience about the system, it is able to continuously improve the control signal to
reduce the vibrations more effectively. The network is capable of working with linear or
non-linear systems and adjusts itself to changing parameters in the system.
Related Links
Learn more about this neural
networking software
The
robots of the lab, including movies of our talented biped!
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Researches | the | problem | of | vibration | control | in | robotics | through | real-time | control | experiments | and | simulation. |
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http://www.ece.unh.edu/robots/rbt_home.htm
UNH Robotics Laboratory 2008 August
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Researches the problem of vibration control in robotics through real-time control experiments and simulation.
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