1 dan o. popa, ee 1205 intro. to ee 1 disciplines in ee controls and robotics dan popa, ph.d.,...

1Dan O. Popa, EE 1205 Intro. to EE 1

Disciplines in EEControls and Robotics

Dan Popa, Ph.D., Associate [email protected], http://ngs.uta.edu

• Systems Approach and Related Concepts - Review• Feedback Control Basics• Feedback Control History• Robotics Basics• Robotics History• Examples

mailto:[email protected]

http://ngs.uta.edu/


Signals and Systems

– Signal: • Any time dependent physical quantity• Electrical, Optical, Mechanical

– System:• Object in which input signals interact to

produce output signals.• Some have fundamental properties that make

it predictable: – Sinusoid in, sinusoid out of same frequency (when

transients settle)– Double the amplitude in, double the amplitude out

(when initial state conditions are zero)?

x(t)

u(t) y(t)


System Modeling

• Building mathematical models based on observed data, or other insight for the system.– Parametric models (analytical): ODE, PDE– Non-parametric models: graphical models - plots, look-

up cause-effect tables– Mental models – Driving a car and using the cause-

effect knowledge– Simulation models – Many interconnect subroutines,

objects in video game


Types of Models

• White Box – derived from first principles laws: physical,

chemical, biological, economical, etc.– Examples: RLC circuits, MSD mechanical models

(electromechanical system models).• Black Box

– model is entirely derived from measured data– Example: regression (data fit)

• Gray Box – combination of the two


White Box Systems: Electrical

• Defined by Electro-Magnetic Laws of Physics: Ohm’s Law, Kirchoff’s Laws, Maxwell’s Equations

• Example: Resistor, Capacitor, Inductor

u

Riu

i

C

ui

L


RLC Circuit as a System

Kirchoff’s Voltage Law (KVL):

u1

L

C

R

uu3

u2RLCq(t)

u(t) i(t)


Linear Time-Invariant Models

• Continuous-time linear dynamical system (LDSC) has the formdx/dt= A(t)x(t) + B(t)u(t), y(t) = C(t)x(t) + D(t)u(t)

• where:– t R denotes time– x(t) Rn is the state (vector)– u(t) Rm is the input or control– y(t) Rp is the output


Linear Systems in Practice

• most linear systems encountered are time-invariant: A, B, C, D are constant, i.e., don’t depend on t– Examples: second-order electromechanical systems with constant

coefficients

• when there is no input u (hence, no B or D) system is called autonomous– Examples: filters, uncontrolled systems

• when u(t) and y(t) are scalar, system is called single-input, single-output (SISO)

• when input & output signal dimensions are more than one, MIMO– Example: Aircraft – MIMO


Linear System Description in Frequency Domain

• Purpose of Frequency Domain Analysis:• Convert Differential equations into Algebraic Equations• Interconnect systems using block diagrams• Use graphical tools to discover of influence behavior of systems


EE-Specific Diagrams

• Block Diagram Model: – Helps understand flow of information (signals) through a complex system– Helps visualize I/O dependencies– Equivalent to a set of linear algebraic equations.– Based on a set of primitives:

Transfer Function Summer/Difference Pick-off point

H(s)U(s) Y(s)

+

+

U2

U1 U1+U2 U U

U


Control System Block Diagram


Automatic Control

• Control: process of making a system variable converge to a reference value

• If r=ref_value=changing - servo (tracking control)• If r=ref_value=constant - regulation (stabilization)• Open loop vs. closed loop (feedback) control

ControllerK(s)

PlantG(s)

+

-

Sensor GainH(s)

++

ControllerK(s)

PlantG(s)

r

r

y y


Brief History of Feedback Control

• The key developments in the history of mankind that affected the progress of feedback control were:

• 1. The preoccupation of the Greeks and Arabs with keeping accurate track of time. This represents a period from about 300 BC to about 1200 AD. (Primitive period of AC)

• 2. The Industrial Revolution in Europe, and its roots that can be traced back into the 1600's. (Primitive period of AC)

• 3. The beginning of mass communication and the First and Second World Wars. (1910 to 1945). (Classical Period of AC)

• 4. The beginning of the space/computer age in 1957. (Modern Period of AC).


Primitive Period of AC

Float Valve for tank level regulators Drebbel incubator furnace control (1620)(antiquity)


Primitive Period of AC

James Watt

Fly-Ball Governor

For regulating steam engine speed

(late 1700’s)


Classical Period of AC

• Stability Analysis: Maxwell, Routh, Hurwitz, Lyapunov (before 1900).• Electronic Feedback Amplifiers with Gain for long distance

communications (Black, 1927) – Stability analysis in frequency domain using Nyquist criterion (1932),

Bode Plots (1945).

• PID controller (Callender, 1936) – servomechanism control• Root Locus (Evans, 1948) – aircraft control• Most of the advances were done in Frequency Domain.


Modern Period of AC

• Time domain analysis (state-space)• Bellmann, Kalman: linear systems (1960)• Pontryagin: Nonlinear systems (1960) – IFAC• Optimal controls• H-infinity control (Doyle, Francis, 1980’s) – loop shaping (in

frequency domain).• MATLAB (1980’s to present) has implemented math behind

most control methods.


Feedback Control

• Role of feedback:– Reduce sensitivity to system parameters (robustness)– Disturbance rejection– Track desired inputs with reduced steady state errors,

overshoot, rise time, settling time.• Systematic approach to analysis and design

– Select controller based on desired characteristics• Predict system response to some input

– Speed of response (e.g., adjust to workload changes)• Approaches to assessing stability


Feedback System Block Diagram

• Temperature control system


Feedback System Block Diagrams

• Automobile Cruise Control

21Dan O. Popa, EE 1205 Intro. to EE

Block Diagram of Feedback

PlantControllerS)(sU

)(sN

Disturbance

)(sY

)(sR)(sE

Transducer

)(sB

+

–

S

)(1 sG )(2 sG

)(sH

Reference Value


Key Transfer Functions

)()()(

)(

)(

)(

)(

)(21 sGsG

sE

sU

sU

sY

sE

sY :eedforwardF

)()()()(

)( :Loop 21 sHsGsGsE

sB )()()(1

)()(

)(

)( :

21

21

sHsGsG

sGsG

sR

sY

Feedback

PlantControllerS)(sU

)(sY

)(sR)(sE

Transducer)(sB

+

–

)(1 sG )(2 sG

)(sH

Reference


Transient Response Characteristics

statesteady of % specified within stays time Settling :

reached is valuepeak whichat Time :

valuestatesteady reachfirst untildelay time Rise :

valuestatesteady of 50% reach untilDelay :

s

p

r

d

t

t

t

t

0.5 1 1.5 2 2.5 3

0.25

0.5

0.75

1

1.25

1.5

1.75

2

overshootM p

stptrtdt


Effect of pole locations

Faster Decay Faster Blowup

Oscillations(higher-freq)

Im(s)

Re(s)(e-at) (eat)

inout VA

AV

1

Negative feedback Pole at -1/A (stable)

inout VA

AV

1

Positive feedback Pole at 1/A (unstable)


Basic Control Actions: u(t)

:control alDifferenti

:control Integral

:control alProportion

sKsE

sUte

dt

dKtu

s

K

sE

sUdtteKtu

KsE

sUteKtu

dd

it

i

pp

)(

)()()(

)(

)()()(

)(

)()()(

0


Summary of Basic Control• Proportional control

– Multiply e(t) by a constant• PI control

– Multiply e(t) and its integral by separate constants– Avoids bias for step

• PD control– Multiply e(t) and its derivative by separate constants– Adjust more rapidly to changes

• PID control– Multiply e(t), its derivative and its integral by separate constants– Reduce bias and react quickly


Conclusion: Control Systems

• Abstraction is the basis for system level thinking. Abstraction requires advanced mathematics, and it is especially required of Electrical and Computer Engineers.

• Control Theory contains abstractions and generalizations able to guarantee predictable performance of systems under control.

• Negative feedback offers numerous advantages: noise rejection, robustness to plant variations, dynamical tracking performance.

• Examples of popular control schemes include Proportional-Integral-Derivative (PID) schemes.

• Modern control is primarily based on time-domain analysis of state-equations using matrices.

• Control engineers can find jobs in any industry. Control concepts can be applied in any engineering industry.


Robots as Complex Systems Controlled by Feedback

G. Bekey definition: an entity that can sense, think and act.Classification: manipulators, mobile robots, mobile manipulators.

Sense Think Act

Robot


Robot Subsystems• A mechanical structure.

– For manipulators this structure consists of a set of rigid bodies (links), connected by means of articulations (joints). Links and joints can also be described in terms of an arm (for mobility), a wrist (for dexterity) and an end-effector (for performing the task).

– For mobile robots, the structure consists of a chassis with a locomotion mechanism, in the form of legs, wheels, rotor blades, etc.

• Actuators. These set the robot in motion through actuation of its joints, and are typical electric or hydraulic.

• Sensors. These measure the status of the manipulator (propriceptive sensors) and the status of the environment (heteroceptive sensors).

• A control system. This enables control and supervision of the robot, and is usually a computer with a graphical user interface, a pendant, or a remote controller.


Mechanics of Manipulators• We describe robotic manipulators in terms of their degrees of freedom (DOFs).

– 6 DOFs are needed to position and orient an object in a unique way in the 3D space. • Most robots have no more than 6 degrees of freedom. If they do, they are called

redundant robots. Redundant robots can be ideal for situations requiring reaching out behind certain obstacles.

• The manipulator links are connected together in chains. Chains can be open or closed.

• Manipulators with open chains are also called serial, while the ones with closed chains are called parallel.

• Joints allow relative motion between links, and can be rotary (revolute – R ) or linear (prismatic –P ).

• The workspace of the manipulator is the total volume swept out by the end-effector of the manipulator.

– The workspace may be constrained by the fact that not all joints can rotate 360 degrees. – The workspace is defined in terms of point reachable with arbitrary orientations

(dextrous workspace) or fixed orientations (reachable workspace).


Typical Industrial Robot

• 6 DOFs• Controller


Examples of industrial manipulator geometries

Revolute“RRR”



Cartesian“PPP”



• “SCARA”• 3R+P



• Parallel• Stewart platform


Mobile Robots• Wheeled (incl. tracks)• Legged• Aerial• Underwater


Mobile Robotic Wheels


Mobile Robot Tracks


Wheeled Robot Configurations


Mobile Manipulators


Basic Concepts• In robotics we are constantly concerned with the location of objects in 3D

space. – In order to describe it we attach a coordinate frame rigidly to an object, or to

the manipulator. We then transform the position and orientation from one frame to another. The frame associated with the non-moving parts of the manipulator is called the base frame, and the one attached to the end-effector is called the tool frame.


Basic Concepts• Kinematics is the science of motion based on geometric description, regardless of

the forces which cause it. Kinematics deals with positions and its derivatives (velocity/acceleration).

• The number of DOFs of the manipulator equals the number of independent position variables that would have to be specified in order to locate all parts of the mechanism. It equals the number of joints in an open kinematic chain.

• Forward Kinematics refers to the problem of computing the position and orientation of the end-effector relative to the base frame given a set of joint angles.

• Cartesian space (or task space, operational space) is the usual 3D Euclidian space for position and orientation (6 DOFs). The joint space (or configuration space) is the space in which the manipulator is described by it’s joint angles.

• Inverse kinematics is the problem of inverse mapping between end-effector positions and orientation and the joint angles. We need to map locations in task space to the robot’s internal joint space.


Basic Concepts• Dynamics is devoted to studying the forces required to cause motion.

– The relationship between the joint actuator torques, the accelerations of the robot, and the other external forces (gravity of links and payload, external forces exerted) is studied within the context of dynamics.

– Dynamics is important if we use high velocities to actuate the system. – If there is no motion involved, the force/torque balancing analysis is also called

manipulator statics– Kinematics is usually sufficient if the robot is gravity compensated and moves at slow

speeds. – Dynamics is necessary for simulation and control.

• Motion planning refers to the study of generating motion for the robot to accomplish a task. This consists of :

– Path planning - generating a feasible path from an initial position to a final position by describing the geometric position and orientation of the robot during the transition. Sometimes this path must avoid obstacles in the task space, and it may be described by intermediate points (also called via-points). Sometimes the path is a spline (e.g. a smooth function that passes through a set of via points).

– Trajectory generation – attaching a time frame to the paths generates a trajectory. The trajectory not only describes the position of the robot during motion, but also how that position changes with time.


Basic Concepts• Manipulator control refers to a closed-loop feedback system that uses sensory

information to control the motion of the manipulator. A controller accomplishes :– Trajectory tracking – following the prescribed trajectory for the manipulation.– End-point control - reaching a goal configuration in either task or joint space

irrespective of the trajectory it is achieved. This is also called the stabilization problem.– Position/velocity control – compensates for errors in knowledge of the systems

parameters and suppresses disturbances. Control algorithms can be linear or nonlinear.– Force control – Controlling the force exerted by the manipulator onto an object in a

single or multiple degrees of freedom. Can be reduced to position control if the stiffness of the manipulator and object are known, but it usually requires force sensing. Sometimes a scheme called hybrid control is used, e.g. controlling force along certain DOFs and position along other DOFs.

• Robot Programming – Modern robots use robot programming languages to describe tasks from users. Programming could be on-line (with the robot attached) and off-line (with a dynamic simulation model of the robot). The issue of safety should be carefully considered when implementing on-line robot motion. Often time robotic cells have interlocked protective enclosures and fences.


History of Robotics• Robotics was first introduced into our vocabulary by Czech playwright Karel Capek

in his 1920’s play Rossum’s Universal Robots.

• The word “robota” in Czech means simply work. Robots as machines that resemble people, work tirelessly, and revolt against their creators.

• The same myth/concept is found in many books/movies today:– “Terminator”, “Star-Wars” series.– Mary Shelley’s 1818 Frankenstein.

• Frankenstein & The Borg are examples of “cybernetic organisms”.

• Cybernetics is a discipline that was created in the late 1940’s by Norbert Wiener, combining feedback control theory, information sciences and biology to try to explain the common principles of control and communications in both animals and machines.

• “Behavioral robotics”: organisms as machines interacting with their environment according to behavioral models.


Manipulators• Industrial manipulators were born after WWII out of earlier

technologies: – Teleoperators. Teleoperators, or remotely controlled mechanical

manipulator, were developed at first by Argonne and Oak Ridge National Labs to handle radioactive materials. These devices are also called “master-slave”, and consisted of a “master” arm being guided through mechanical links to mimic the motion of a “slave” arm that is operated by the user. Eventually, the mechanical links were replaced by electrical or hydraulic links.

– Numerically controlled milling machines (CNC). CNC machines were needed because of machining needs for very complex and accurate shapes, in particular aircraft parts.


Mobile Robots• Mobile robots were born out of “unmanned vehicles”, which also appear

in WWII (for example an unmanned plane dropped the atomic bomb at Nagasaki).

• Unmanned Aerial Vehicles (UAV), Underwater Vehicles (UUV) and Ground Vehicles (UGV).

• Because tethered mobile vehicles could not move very far, and radio communications were limited, an approach to mobile robots is to endow them with the necessary control and decision capability - “autonomy”

• Autonomous Underwater/Ground/Aerial Vehicles (AUV/AGV/AAV).

• Unlike manipulators, we do not think of a remotely controlled toy as a mobile robot, suggesting that one of the fundamental aspects of mobile robotics is the capacity for autonomous operation.


Robot History Timeline• 1947-1949 – first electric and hydraulic teleoperators are developed by

General Electric and General Mills. Force feedback is added to prevent the crushing of glass containers during manipulation.

• 1949 - CNC machine tools for accurate milling of aircraft parts are introduced.

• 1953 – W. Grey Walter applies cybernetics principles to a robotic design called “machine speculatrix”, which became a robotic tortoise. The simple principles involved were:– Parsimony: simple is better. Simple reflexes are the basis of robot behavior.– Exploration or speculation: the system never remains still except when

recharging. Constant motion is needed to keep it from being trapped.– Attraction: the system is motivated to move towards objects or light.– Aversion: the system moves away from certain objects, such as obstacles.– Discernment: the system can distinguish between productive and

unproductive behavior, adapting itself to the situation.


G. Walter Grey's tortoise

These vehicles had a light sensor, touch sensor, propulsion motor, steering motor, and a two vacuum tube analog computer.


Robot History Timeline• 1954 – George Devol replaced the slave manipulator in a teleoperator with the

programmability of the CNC controller, thus creating the first “industrial robot”, called the “Programmable Article Transfer Device”.

• 1955 – The Darmouth Summer Research Conference marks the birth of AI. Marvin Minsky, from the AI lab at MIT defines an intelligent machine as one that would tend to “build up within itself an abstract model of the environment in which it is placed. If it were given a problem, it could first explore solutions within the internal abstract model of the environment and then attempt external experiments”. This approach dominated robotics research for the next 30 years.

• 1956 - Joseph Engleberger, a Columbia physics student buys the rights to Devol’s robot and founds the Unimation Company.

• 1961 – The first Unimate robot is installed in a Trenton, NJ General Motors plant to tend a die casting machine. The key was the reprogrammability and retooling of the machine to perform different tasks. The Unimate robot was an innovative mechanical design based on a multi-degree of freedom cantilever beam. The beam flexibility presented challenges for control. Hydraulic actuation was eventually used to alleviate precision problems.


UNIMATE robot


Robot History Timeline• 1962 – 1963 – The introduction of sensors is seen as a way to enhance the

operation of robots. This includes force sensing for stacking blocks (Ernst, 1961), vision system for binary decision for presence of obstacles in the environment (McCarthy 1963), pressure sensors for grasping (Tomovic and Boni, 1962). Robot interaction with an unstructured environment at MIT’s AI lab (Man and Computer – MAC project).

• 1968 – Kawasaki Heavy Industries in Japan acquires a license for Unimate.

• 1968 – Shakey, a mobile robot is developed by SRI (Stanford Research Institute). It was placed in a special room with specially colored objects. A vision system would recognize objects and pushed objects according to a plan. This planning software was STRIPS, and it maintained and updated a world model. The robot had pan/tilt and focus for the camera, and bump sensors.

• 1971 -1973 – The Stanford Arm is developed, along with the first language for programming robots - WAVE.


Robot History Timeline• Late 1970’s – First assembly applications of robotics are considered: water pumps

– Paul and Bolles, typewriter – Will and Grossman, Remote Center of Compliance gripper (RCC) developed at Draper Labs.

• 1970’s – Innovation in the type of robots introduced: Unimation 2000, Cincinnati Milacron (“The tomorrow tool, T3”) – the first computer controlled manipulator, the PUMA (“Programmable Universal Machine for Assembly”) by Unimation, the SCARA (“Selective compliant articulated robot for Assembly”) introduced in Japan and the US (by Adept Technologies).

• 1972 – First snake-like robot – ACM III – Hirose – Tokyo Inst. Of Tech.

• 1977 – Development of mobile robot Hilaire at Laboratoise d’Automatique et d’Analyse des Systemes (LAAS) in Toulouse, France. This mobile robot had three wheels and it is still in use.

• 1970’s – JPL develops its first planetary exploration Rover using a TV camera, laser range finder and tactile sensors.


Snake-like robot

A. Hirose (Tokyo IT)


Robot History Timeline• 1980’s – Innovation in improving the performance of robot arms – feedback

control to improve accuracy, program compliance, the introduction of personal computers as controllers, and commercialization of robots by a large number of companies: KUKA (Germany), IBM 7535, Adept Robot (USA), Hitachi, Seiko (Japan).

• Early 1980’s – Multi-fingered hands developed, Utah-MIT arm (16 DOF) developed by Steve Jacobsen, Salisbury’s hand (9 dof).

• 1977-1983 – Stanford cart/CMU rover developed by Hans Moravec, later on became the Nomad mobile robot.

• 1980’s – Legged and hopping robots (BIPER – Shimoyama) and Raibert 1986.

• 1984 -1991 – V. Braitenberg revived the tortoise mobile robots of W. Grey Walter creating autonomous robots exhibiting behaviors. Hogg, Martin and Resnick at MIT create mobile robots using LEGO blocks (precursor to LEGO Mindstorms). Rodney Brooks at MIT creates first insect robots at MIT AI Lab – birth of behavioral robotics.


IBM 7535

• IBM 7535 Manufacturing System provided it advanced programming functions, including data communications, programmable speed.


Utah-MIT arm


Robot History Timeline• 1990’s – Humanoid robots – Cog, Kismet (MIT), Wasubot, WHL-I – Japan,

Honda P2 (1.82m, 210kg), and P3 (1.6m, 130kg), ASIMO.

• 1990’s – Entertainment and Education Robots – SARCOS (“Jurassic Park”), Sony AIBO, LEGO Mindstorms, Khepera, Parallax.

• ROBOCUP, the competition simulating the game of soccer played by two teams of robots having been held around the world since 1997 (Osaka) .

• 1990’s – Introduction of space robots (manipulators as well as rovers – the MARS rover 1996), parallel manipulators (Stewart-Gough Platforms), multiple manipulators, precision robots (“Robotworld”), surgical robots (“RoboDoc”), first service robots (as couriers in hospitals, etc)


Asimo

Honda announced the development of new technologies for the next-generation ASIMO humanoid robot, targeting a new level of mobility.

•

http://world.honda.com/HDTV/ASIMO/200412-announce/

http://world.honda.com/HDTV/ASIMO/200412-announce/

http://world.honda.com/ThePowerofDreams/run/


Entertainment robots from SARCOS


Kismet – MIT AI Lab

• Kismet consists of a head with large eyes with eyelids, bushy eyebrows, rubber lips, and floppy ears.


Robot History Timeline• 2000’s – IRobot introduces the first autonomous vacuum –

“Roomba”.

• 2000’s – Mini and micro robots, “Smart Dust” – Pister @ Berkeley, UTA, EPFL/Lausanne, microfactories.

• 2000’s – Military applications - Robotic assistants for dangerous environments and reconnaissance, AUV’s and UUV’s, etc.

• 2000’s – Intuitive Surgical introduces the Da Vinci surgical robot.

• 2000’s – Robotic Deployment of Sensor Networks


2006- Microsoft Introduces MSRobotics Studio

What is Microsoft Robotics Studio? A window-based environment that is used to create robotics application

What does Microsoft Robotics Studio do? Consider Robotics Application where we have several sensory inputs andneeded to be processed to command Actuators output

Microsoft Robotics Studio provide a programmatic way to create anorchestrator that manage robotics applications (“Service”)

Inputs Actuators

Multiple Sensory InputsMultiple Actuator OutputsOrchestrator

Orchestration: “The task of consuming sensory input from a variety of sources and as a result manipulating a set of actuators

to respond to the sensory input.”


Recent Developments in Robot Software Platforms

• 2009- Willow Garage Launches Robot Operating System (ROS)– http://www.youtube.com/user/WillowGaragevideo#p/search/5/ueAByx7zQrg

• 2011- National Instruments Introduces the Labview Robotics Module – http://sine.ni.com/nips/cds/view/p/lang/en/nid/209856

http://www.youtube.com/user/WillowGaragevideo#p/search/5/ueAByx7zQrg

http://sine.ni.com/nips/cds/view/p/lang/en/nid/209856


USC Mobile Robots

Robot teams (A. Howard)


Flying Insect (UCB)


Solar AUV II

SAUV-II from Autonomous Underwater Research Institute (AUSI) – New Hampshire


Hierarchical family of robots (K-Team - Switzerland)

Koala (20 in)

Khepera (6 in)

Alice (1 in)


IEEE Robotics Societies

• Website to watch:– IEEE Robotics & Automation Society

• Join it• Check out events and information at: http://www.ieee-ras.org/

– IEEE Region 5 Robotics Competition• Both a paper and a robot course contest.• http://www.engr.uark.edu/2007ieeer5conference/comp_robotics.

php

– NIST Microrobotics Challenge @ IEEE ICRA• Compete with the world’s smallest robots• http://www.nist.gov/pml/semiconductor/mmc/

http://www.ieee-ras.org/

http://www.engr.uark.edu/2007ieeer5conference/comp_robotics.php

http://www.engr.uark.edu/2007ieeer5conference/comp_robotics.php

http://www.nist.gov/pml/semiconductor/mmc/


Conclusion: Robotics

• Robotics uses advanced concepts in control to connect sensors with actuators.

• Robots can be classified as manipulators (e.g. robotic arms), mobile robots, mobile manipulators.

• Major disciplines in robotics are: kinematics, dynamics, planning, control, perception, and cognition.

• Robotics is a multidisciplinary field, including computer scientists, mechanical engineers, electrical engineering, industrial engineers, etc., but the largest robotics society in the world is in IEEE.


Research in Multiscale Robotics atNext Gen Systems (NGS) Group

Robotics

Control Systems

Manufacturing & Automation

Established Technologies Emerging Technologies

Micromanufacturing Microrobotics Microassembly MicropackagingSensors & ActuatorsNanoManufacturing

Microsystems & MEMS

Nanotechnology

Biotechnology

Small-scale Robotics & Manufacturing

Modeling & Simulation

Control Theory

Algorithms

Tools and Fundamentals

Sensor networks

Surgical robotics

Human-like robots

Distributed systems

New applicationsfor small-scale systems


NGS Research

• Micro and Nano Robotics– Manufacturable Micro and Nano Robotics

• Automated MEMS Assembly and Packaging– Mobile Microrobotics

• Sub-Millimeter size robots powered by ambient fields

• Next Generation Robotics for Healthcare– Assistive Robotics

• Treatment of cognitive and motor disabilities (Autism, CP) using Advanced Human-Robot Interaction (HRI)

– Microrobotics for healthcare application (in-vivo or in-vitro manipulation and process tools)

• Examples from recent projects – Micro Robotic Factories– UTA Microrobotics Team – Assistive Robots


N³ - Wafer Scale Microfactory (Micro-Nano)

“From a few robots+controllers to many µrobots via assembly and die bonding”

Controller + Robot µparts, nparts in

µparts, nparts in

Assemblies out

µrobot MEMS dies

µcontroller IC dies


N³ - Wafer Scale Microfactory for Nanotechnology


Making the Microfactory by Automated 3D Microassembly

Control Challenges:-Larger number of robots

- Measurement uncertainty, measurement range,

- Time delays

- Fewer embedded sensors, low SNR

- Manufacturing uncertainty, inacurate robot models)

- Environmental effects (stiction, temperature)


NIST Microbotics Challenge 2011• Hosted at IEEE International Conference on Robotics and Automation, Shanghai, China, May 10, 2011. • 7 Qualified Teams: France (FEMTO-ST), Italy (IIT), Univ. of Waterloo (CA), 4 US Universities (Stevens,

Hawaii, Maryland, UTA• Maximum robot size: 600 microns sphere.

MobilityChallenge

MicroAssemblyEvent

Vibration and Laser Actuated

UTA Microrobots, 2011


UTA Vibot Control Using National Instruments PXI-8196

• Microrobot pose (x, y, θ) from NI-1742 Smart Camera• Exchange of pose data with the control interface VI via shared variables• User control of square wave output through PXI-5201 Arbitrary Waveform

Generator (AWG). Output frequency to piezoelectric actuator. PXI 7831 FPGA RIO• Data logging via control interface VI• UTA Microrobotics Team video

square waveamplitude & frequency

PXI-8196 controllerrobot posex, y, θ

PZT Actuator

arena andmicrorobot

image

user control

control interface VI

http://www.youtube.com/watch?v=GZMUZ1aeLD8


Realistic & Intuitive Human-Robot

Interaction

Co-botics w/ Physical Interaction

Real-Time Visual Feedback and Facial

Expressions

Advanced Human-Robot Interfaces

Advanced Human Robot Interaction

Zeno Video

Neptune Control through Neural Headband

Robot Touch HRI

Visual HRI


Land-Based Mobile Wireless Sensors


Hubo (KAIST, Hanson Robotics, Inc. and ARRI) - 2005

Face uses – 38 motors

Advanced AI

Simulates Expression in Real time

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