University of New Hampshire
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Robotics and vibration control 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.). |
Product Portfolio
The General Electric P-5 Robot
- The General Electric P-5 five axis articulated robotic
arm shown above is used at UNH for experiments studying high performance
dynamic control. Typical of industrial manipulators, this arm is capable of
high accelerations and accurate positioning. The arm is driven by 100 volt
DC motors with discrete position encoders (0.01 degree resolution) and
analog tachometers. A work cell for the P-5 robot including a variable speed
conveyor and video feedback is available for kinematic experiments involving
interactions within a moving frame of reference. A general purpose
sensory/command interface has been designed for this robot, providing direct
access to the motor drivers and sensory feedback via a 100 MHz 80486 based
workstation. The basic software modules necessary for real time control
experimentation (actuator and sensor device drivers, fixed rate interrupt
drivers, software timing monitors, data loggers, data display routines,
etc.) have been developed for the Microsoft Windows NT operating system. New
control or neural network techniques can be evaluated by merely replacing
those software modules responsible for the specific control computations
(generally written in the C/C++ languages). Thus, given our existing
foundation of hardware and software, it is generally as easy to test a new
concept in a real time control experiment as it is to test the same concept
in simulation (from the standpoint of software development effort).
Typically, the same learning system software modules are used for both
simulation studies and real time experiments.
Two Scorbot Manipulators With Active Stereo Vision
- The image above shows two Scorbot ER-V five axis
table-top robotic arms with two-fingered grippers, for experiments requiring
multi-arm cooperation. A Rhino XR-II arm (substantially modified
mechanically and electrically at UNH) serves as a moving base for a
binocular vision system, supporting related experiments in active sensing
and three dimensional hand-eye coordination. The gripper axis of the Rhino
arm has been modified to provide true binocular vision based depth
perception through computer control of the parallax angle. All three of
these table-top robots utilize DC motors with variable speed motor drivers
and high resolution position encoders. Image acquisition and storage is
performed using a ITI FG-100 image processing system with dual frame
grabbers and buffers. Image processing and control operations are performed
on a 100 MHz 80486 based workstation running the Microsoft Windows NT
operating system.
The UNH Biped Robot
- The biped hardware developed at UNH is shown above. The
goal of the ongoing ARPA/ONR sponsored research is to develop strategies for
the control of static and dynamic balance during two legged walking based on
a hierarchy of simple gait oscillators, PID controllers and neural network
learning, but requiring no detailed dynamic models. The biped is
approximately 61 cm tall from foot to hip, and 43 cm tall from hip to the
top of the body. The separation between the legs is 20 cm. Each foot (a flat
metal plate) is 7 cm wide and 12 cm long, with the ankle attached near the
center-rear corner of the foot. The biped weighs approximately 25 pounds.
Each hip and ankle is actuated by two gearmotors, one for rotation of the
leg towards the front of the biped and one for rotation towards the side.
Each knee is actuated by a single gearmotor. The ten gearmotors are driven
by 12 volt pulse-width-modulated (PWM) motor drivers. The positions of the
ten joints are sensed by optical position encoders on the gearmotors.
Polymer thick film force sensing resistors are mounted on the underside of
each foot, near each corner (four 1" diameter sensors per foot). Each sensor
is sandwiched between the upper metal foot plate and a thin disc of rubber,
which in turn is bonded to a semi-rigid Plexiglas and rubber bottom plate.
Two piezoresistive accelerometers oriented along orthogonal horizontal axes
are mounted near the top of the body in order to provide two-dimensional
body acceleration sensing (it is assumed that the vertical body acceleration
is dominated by the constant gravitational term). All PWM and sensor
circuits are interfaced to a single Siemens 20 Mhz 80C166 16-bit
microcontroller. This microcontroller performs sensor and actuator
management, low level PD actuator control, and communicates with the host
processor over a 57.6 Kbaud serial communications line. High level control
computations are currently carried out on a single 200 MHz PentiumPro
personal computer running the Microsoft Windows NT real-time multi-threaded
operating system. This processor is responsible for communications with the
biped microcontroller, for gait and balance control computations, for neural
network computations, and for the user command and status interface.
The UNH Quadruped Robot
- The quadruped hardware developed at UNH is shown above.
Again, the goal of the ongoing ARPA/ONR sponsored research is to develop
strategies for the control of dynamic balance during four legged walking
based on a hierarchy of simple gait oscillators, PID controllers and neural
network learning, but requiring no detailed dynamic models. The quadruped is
approximately 61 cm tall from foot to hip, and 64 cm from front to back. The
separation between the legs is 20 cm. The quadruped weighs approximately 50
pounds. It is essentially two complete copies of the biped legs, hips,
sensors and electronics, connected by a rigid spine. All PWM and sensor
circuits are interfaced to two Siemens 20 Mhz 80C166 16-bit
microcontrollers. These microcontrollers perform sensor and actuator
management, low level PD actuator control, and communicates with the host
processor over a single 57.6 Kbaud serial communications line. High level
control computations are currently carried out on a single 90 MHz Pentium
class personal computer running the Microsoft Windows NT real-time
multi-threaded operating system. This processor is responsible for
communications with the biped microcontroller, for gait and balance control
computations, for neural network computations, and for the user command and
status interface.
The UNH Wheeled Mobile Robot
- The image above shows the wheeled mobile robot developed
by undergraduate students as part of the Research Experiences for
Undergraduates (REU) Site sponsored at UNH by the National Science
Foundation. The mechanical system is an overhauled Heathkit Hero 2000 robot
with an added array of six Polaroid ultrasonic range finders. The
electronics of the original Hero were completely removed by the students and
replaced by interfaces of their design to standard computer platforms.
Sensor management and low level motor control are performed by a Siemens 20
Mhz 80C166 16-bit microcontroller. High-level navigation, planning and
control are performed on a 80386 based notebook computer running the
Microsoft Windows 95 operating system. The microcontroller communicates with
the notebook computer over a single 38.4 Kbaud serial communications line.
The complete system is capable of operating for approximately one hour when
roaming untethered about Morse Hall, or indefinitely on a power tether
within the Robotics Laboratory.
The Liberty Technology Boston Elbow
- Students at UNH are also working on developing adaptive
myoelectric control channels for the Liberty Technology Boston Elbow shown
above. The goal is to overcome issues of subject-to-subject and day-to-day
myoelectric signal variability using on-line adaptation of the control
channel, borrowing from the neural network learning techniques developed at
UNH for traditional robotics applications. For research purposes, the normal
analog command input signals to the Boston Elbow have been interfaced to D/A
outputs on a 100 Mhz 80486 workstation, and the outputs of high-performance
EMG amplifiers have been interfaced to A/D inputs on the same computer.
Custom device drivers have been written for these interfaces under the
Microsoft Windows NT operating system. Experimental control code can then be
written in the C/C++ languages, taking advantage of the multi-threaded
nature of Windows NT and making use of control and neural network learning
code modules developed in the laboratory for other applications.