Presentation on theme: "EE 4315/EE5325 Robotics Lectures: Tue-Thu, 11-12:20 pm, NH111 Instructors: Indika Wijayasinghe Ph.D. & Dan Popa, Ph.D., Associate Professor, EE Office."— Presentation transcript:
EE 4315/EE5325 Robotics Lectures: Tue-Thu, 11-12:20 pm, NH111 Instructors: Indika Wijayasinghe Ph.D. & Dan Popa, Ph.D., Associate Professor, EE Office hours: Tue/Thu 9:00 –11:00 pm, NH250 Tue/Wed 1:30pm-3:30 pm, NH543 Course info: Grading policy: Homework – 15% Midterm exam (take home) – 20% Final project w/ presentation – 40% Final exam (in class) – 25% Project grade is composed of: –Project Proposal (February 12) – 5% –Project Progress Report (March 19) – 10% –Project Final Presentation (May 5, May 7) – 5% –Project Report / Paper (May 7) – 20%
Summary This is a project-based robotics course, containing both introductory as well as more advanced concepts. It presents a broad overview of robotic manipulation and also focuses on fundamentals such as robot kinematics, dynamics, control and planning. This year, the course will focus on concepts in support of major competitions. The course is divided between two areas: –Robot Kinematics Rotations, Translations and Homogeneous Transformations Parameterizations and Singularities Robot kinematics and the Jacobian Robot statics Multi-fingered grasping Nonholonomic and parellel robots –Robot Dynamics and Control Robot dynamical models Computed Torque Control Advanced Robot Control Kinematic Control of Nonholonomic Robots Trajectory Planning Force Control and Physical Human Robot Interaction Visual Servoing and Visual Human Robot Interaction
Tentative Syllabus Week 1 - Jan 20, 22, Lectures 1,2 –Intro to robotics: History of robotics ; Examples of robots –Robotics Jargon ; Course Projects Week 2 - Jan 27, 29 Lectures 3,4 –Review of basics: Matrix algebra –Intro to Robot Kinematics: Geometric issues ; Frames ; Notation Week 3 - Feb 3, 5, Lectures 5,6 –Homogeneous Transforms, Parameterizations and Singularities Week 4 -Feb 10, 12, Lectures 7,8 –Robot Kinematics : DH framework ; Forward Kinematics ; Inverse Kinematics –Discussion on other Kinematic Representations
Tentative Syllabus Week 5 - Feb 17, 19, Lectures 9, 10 –Robot Manipulability and Singularities; Statics Week 6 - Feb 24, 26, Lectures 11, 12 –Manipulability, redundancy, parallel robots. Week 7 - March 3, 5, Lectures 13, 14 –Robot statics, compliance, stiffness –Robot grasping and multi-fingered manipulation Week 8 - March 9-13 –Spring Break Week 9 - March 17, 19, Lectures 15, 16 –Robot grasping and multi-fingered manipulation –Nonholonomic constraints and wheeled mobile robots.
Tentative Syllabus Week 10 - March 24, 26, Lectures 17, 18 –Robot dynamics: Euler-Lagrange formulation –Robot dynamics: Newton Euler formulation –Midterm (take-home) handed out March 26, due March 31 will cover kinematics and statics Week 11 - March 31, April 2, Lectures 19, 20 –Examples of dynamical equations –Properties of robot dynamic equations Week 12 -April 7, 9 Lectures 21, 22 –Basic robot control, modeling and control of joints. –Concepts in stability, passivity with applications to robot control. Week 13 - April 14, 16, Lectures 22, 23 –Computed torque control. –Trajectory generation and following for manipulators.
Tentative Syllabus Week 14 - April 21, 23, Lectures 24, 25 –Robot Control using Lyapunov Theory –Robust and advanced control for robot manipulators. Week 15 - April 28, 30, Lectures 26, 27 –Introduction to Force control. –Introduction to robot vision and visual servo control Week 16 - May 5, 7, –Project Presentations, Project due Week 17 - May 12 –Final exam (in-class, comprehensive).
Textbooks Robot Modeling and Control, by M.W. Spong, S. Hutchinson, M. Vidyasagar (2005) (required) Mathematical Introduction to Robotic Manipulation by Richard M. Murray, Zexiang Li, S. Shankar Sastry (free download at link) Robot Manipulator Control: Theory and Practice (Control Engineering, 15) by Frank L. Lewis, et al (on reserve) Introduction to Robotics: Mechanics and Control (3rd Edition) by John J. Craig (on reserve) Autonomous Mobile Robots, by S. S. Ge, F. L. Lewis (on reserve). Robotics: Modelling, Planning and Control (Advanced Textbooks in Control and Signal Processing) [Hardcover], Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo ( Publisher: Springer; 2nd Printing. edition (February 11, 2011), ISBN-10: , ISBN-13: MATLAB Robotics Toolbox by Peter Corke, free download ROS and GAZEBO simulation environments, by Willow Garage and OSRF, free download
Course Refresher Reading Assignments Math: linear/matrix algebra, trigonometry, differential equations. Controls: stability, linear & nonlinear systems. Physics: mechanics, force & motion. Programming: MATLAB, ROS Reading assignments will be posted online. Assigned reading material may be useful for certain exam problems.
Honor Code Missed deadlines for take-home exam and project proposal and report: Maximum grade drops 10% per late day Academic Dishonesty will not be tolerated. All exams and reports are individual assignments. Your take-home exam and reports will be carefully scrutinized to ensure a fair grade for everyone. Attendance and Drop Policy: Attendance is not mandatory. However, if you skip classes, you will find the homework and exams more difficult. Assignments are going to be posted here, however, due to the pace of the lectures, copying someone else's notes may be an unreliable way of making up an absence. You are responsible for all material covered in class regardless of absences.
Exams and Class Projects Midterm, final, individual work only. Project – team project only. Course project – A list of topics will be provided. Stop by and discuss the class project with me during office hours early in the semester. The project work is collaborative. It can be applied or can involve programming. IEEE –Amazon Picking Challenge –Mobile Microrobotics Challenge –Humanitarian Robotics Challenge Other possible projects: Prosthetics, Humanoid Robots, and other robots at UTARI and NGS
Humanoid Robots Research Credits: Dr. Woo Ho Lee
ATLAS Biped humanoid robot (28DOF) Height: 1.88m Weight: 150kg Hydraulic actuation Company: Boston Dynamics (purchased by Google) Government Furnished Equipment for DARPA Robotics Challenge Sensors: LIDAR, stereo sensors
THOR Biped humanoid robot (34DOF) Open source platform: Available for purchase after DRC Institution: Virginia Tech Develop for DARPA Robotics Challenge
iCub Humanoid robot for human cognition and AI 53DOF including 18 DOF hands Price: 250K Euros Height: 1m, Weight: 22kg Open source humanoid robot platform – CAD, software, and BOM Institution: IIT funded by EU Adopted by 20 laboratories worldwide. 25 iCubs in Europe, USA, …
Asimo Biped humanoid robot (34DOF) Lease price: $150K / month Height: 1.3m, Weight: 54kg Operation time: 1hour Manufacturer: Honda in Japan Market: Research and education
Hubo II Biped humanoid robot (38DOF) Price: $400K Height: 1.3m, Weight: 50kg Two embedded computers –1 for real time control –1 for communication and application programs (Window XP) Company: – Rainbow Co (KAIST spin-off) in Korea Market: Research and education Used at DARPA Robotics Challenge
HRP-4 Biped humanoid robot (34DOF) Price: $300K Height: 1.5m Weight: 39kg Operation: 34 minute OS: Linux Manufacturer: Kawada industries in Japan onics/hrp4.html onics/hrp4.html
Nao Humanoid robot (25DOF) Price: $16K Open source software Used in the Robot Soccer World Cup Over 200 academic institutions Company: –Aldebaran Robotics in France –Founded in 2005 Market: –Education –Research
DARWIN Open platform Humanoid robot Price: $10K 20 DOFs Used in the RoboCup. Company: –Robotis in Korea –Founded in 1999 Market: –Education –Toy
Zeno Facial expression humanoid robot (36DOF including 11DOF Head) Frubber robotic skin Price: $17.5K Company: –Hanson Robotics –Founded in 2003 Market: Education & entertainment
PR2 Personal robot Open source software: ROS Price: $400K (~50 robots sale) Company: –Willow Garage –Funded by founder. Seek a self sustaining business model –Founded in 2006 Market: Education
Personal and Toy Robots Market
Personal Robotics Affordable Applications –Telepresense –Education –Entertainment Global personal robotics market –$18B in 2015 [Source: Grishin Robotics]
Double iPad equipped teleconferencing robot Price: $1999 (preorder) Company: –Double Robotics –Seed fund from Y Combinator –$250K funding from Grishin Robotics –Founded in 2011 –6 man Market: Telepresence –Hospitals, schools, retail stores, museums 900+ Pre-orders
Sphero Robot Ball You Control With Your Smartphone (iPhone and Android) Price: $129 Company: –Orbotix –Seed fund from TechStars –$15M funding –Founded in 2010 –35 people Bluetooth communication Market: Entertainment President Obama’s visit
Romo Robot with an iPhone for brain Price: $149 Company: –Romotive –Seed fund from TechStars –$6.5M funding –Founded in 2011 –16 people Bluetooth communication Market: Entertainment –Telepresence, Autonomous navigation
Cubelets Robot construction kit Reconfigurable robots Price: $160 - $520 Company –Modular Robotics –$3.5M funding –Founded in 2008 –4 people Market: Education
mOwayduino Arduino based robot Programmable with Arduino IED Startup in Spain –Fund: indiegogo Market: Education and Toy
RK-1 Arduino based mobile robot Arduino + iOS/Android Price: £175 Company: My Mobile Robots –Fund: Kickstarter –One man company in London Market: Entertainment
Disciplines in EE Controls and Robotics Robotics – examples of projects Systems Approach and Related Concepts - Feedback Control Basics Feedback Control History Robotics Basics and History Robotics Projects at NGS
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 linear systems 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)
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
RLC Circuit as a System Kirchoff’s Voltage Law (KVL):
Linear Time-Invariant Models Continuous-time linear dynamical system (LDSC) has the form dx/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) R n is the state (vector) –u(t) R m is the input or control –y(t) R p is the output
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 FunctionSummer/Difference Pick-off point + + U2 U1U1+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 Controller K(s) Plant G(s) + - Sensor Gain H(s) + + Controller K(s) Plant G(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 (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 (performance) 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
Effect of pole locations Faster DecayFaster Blowup Oscillations (higher-freq) Im(s) Re(s) (e -at )(e at ) Negative feedback Pole at -1/A (stable) Positive feedback Pole at 1/A (unstable)
Basic Control Actions: u(t)
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. SenseThinkAct 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.
Recent Developments in Robot Software Platforms for Standardization Willow Garage Launches Robot Operating System (ROS) –http://www.youtube.com/user/WillowGaragevideo#p/search/5/ueAByx7z Qrghttp://www.youtube.com/user/WillowGaragevideo#p/search/5/ueAByx7z Qrg National Instruments Introduces the Labview Robotics Module –http://sine.ni.com/nips/cds/view/p/lang/en/nid/209856http://sine.ni.com/nips/cds/view/p/lang/en/nid/209856
IEEE Robotics Societies and Competitions Websites to watch: –IEEE Robotics & Automation Society Join it for $15 Check out events and information at: –IEEE Region 5 Robotics Competition Both a paper and a robot course contest. For next year (2014) –Mobile Microrobotics IEEE ICRA Compete with the world’s smallest robots
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.
Research in Multiscale Robotics at Next Gen Systems (NGS) Group Robotics Control Systems Manufacturing & Automation Established TechnologiesEmerging Technologies Micromanufacturing Microrobotics Microassembly Micropackaging Sensor & Actuator Arrays NanoManufacturing Microsystems & MEMS Nanotechnology Biotechnology Small-scale Robotics & Manufacturing Modeling & Simulation Control Theory Algorithms Tools and Fundamentals Assistive Robots Human-like robots Distributed and wireless sensor systems New applications for robot systems
NGS Focus: Advanced Robot Systems NGS Motion Control | Human-Robot Interaction | Sensors & Actuators MICRO & Nano MANUFACTURING Packaging | Assembly | Testing | Reliability NEXT GENERATION ROBOTICS Assistive Robotics | Microrobotics | Networked Robotics Learning Control Diagnostics OPTIMIZATION INTEGRATIO N Design Simulation Networking
New Initiatives and Projects at NGS and UTA Research Institute –NSF Projects in Assistive technologies, including robots for helping children with Cerebral Palsy w/ CSE Dept. (2009-). –UTA-UNTHSC joint project to study robot therapy and early diagnosys of Autism Spectrum Disorders – TXMRC consortium (2011-). –UTARI Consortium in Assistive Robotics (2012-) in collaboration with Qinetiq-North America. –UTARI Participation in DARPA Robotics Challenge in collaboration with RESquared, Inc. (Oct. 2012). –New NSF Project through the National Robotics Initiative aiming at creating the next generation Robotic Skin (Oct 2012) for co-Robots. –Plans under way for M.S. Degree program in UAS and Robotics at UTA’s College of Engineering (2013).www.uta.edu/engineering 62
Wafer Scale Microfactory (Micro-Nano) “From a few robots+controllers to many µrobots via assembly and die bonding” Controller+ Robot µparts, nparts in Assemblies out µrobot MEMS dies µcontroller IC dies
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)
Mobile Microbotics Challenge 2011 Hosted at IEEE International Conference on Robotics and Automation, Shanghai, China, May 10, Qualified Teams: France (FEMTO-ST), Italy (IIT), Univ. of Waterloo (CA), 4 US Universities (Stevens, Hawaii, Maryland, UTA Maximum robot size: 600 microns sphere. Mobility Challenge Micro Assembly Event 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 wave amplitude & frequency PXI-8196 controller robot pose x, y, θ PZT Actuator arena and microrobot image user control control interface VI
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 UTA) Face uses – 38 motors Advanced AI Simulates Expression in Real time
Quickskin ™ Basic Demo on 6DOF CRS Robot
New Project: Physical HRI with Robotic Skin Popa, Celik-Butler, Butler, Lewis, Bugnariu National Robotics Initiative 1) Devices: “distributed sensors” Integration of multi-modal, multi- resolution, MEMS skin sensors to include tactile, thermal, pressure, acceleration, and distance IR sensing. 2) System Design: “where to place sensors on robot?” Maximize statistical information metrics related to robot perception. 3) Control and Learning: “both human and robot learn during interaction” Learning algorithms and adaptive impedance control for efficient use of new sensor technology to sense human intent and control the interaction. 4) Co-Robot performance: “how does this technology help humans?” The impact of the new technology to humans will be assessed, including the safety, level of assistance to several targeted user groups, ease of use, aesthetics, and therapeutical benefits of this technology. Robot and Skin Simulation Fabrication & Integration of Skin/Garment Hardware Interaction Learning Perceived Impedance Measurement & Simulation pHRI Iterate Designs & Algorithms Initial sensor prototypes Robotic hardware Task requirements UTA NanoFab Bolometer and Pressure Sensitive Skin
Neptune: Assistive Mobile Manipulation System with intuitive human-machine interfaces Point Cloud overlaid with the virtual model (left), and detected obstacles (right) during dynamic motion planning 73
Neptune: Video Mobile robot control using brain activity sensor device
Zeno – Socially Assistive Robot Zeno is half-meter tall robot-child character capable of facial expression, head-eye motion, arm gesturing, mimicking and dynamic walking in the near future. We are developing a high performance embedded “motor cortex” for Zeno, controlling interactive motion, and applying it for treatment of Autism. 75
RoboKind Zeno R-30 Current Hardware Onboard Computer –4GB NAND flash and 16GB SD Micro Class 10 –1.6GHz Intel Atom Z530 –1GB RAM –WiFi N at 150mb/sec and Giga-bit Ethernet –Ubuntu Linux Two HD cameras –720p, 30 fps Two microphones 18 DOF 4 DOF11 DOF
System Description Robotic Arm Model –4 DOF –Human arm contains 7 DOF Torso –Yaw rotation Neck –Roll, pitch, yaw rotation Eyes –Pan and tilt Eyelids Mouth –Open and close Body PartDOFServo Type Arms8Dynamixel RX-28 Torso1Dynamixel RX-28 Neck3 AX-12 HS-5245MG HS-B1MG Eyes2Cirrus CS- 101 STD Eyelids1Cirrus CS- 101 STD Mouth1HS-65MG
Modes of Operation Dynamic Interactive: Kinect based motion –Can generate a more human-like motion during therapy –Can generate any desired motion with a desired joint velocity generated by the human motion as long as it does not exceed the servo’s velocity constraints –Smooth motion that resembles human-like movement 235 ms robot response time –Human visual response time  ms Dynamic Interactive Mode System Block Diagram Dynamic Interactive Mode Representation
UTA Team Members Dan O. Popa, Ph.D., Associate Prof. of Electrical Engineering Gian Luca Mariottini, Ph.D., Assistant Prof. of Computer Science and Eng. Alan Bowling, Assistant Prof. of Mechanical and Aerospace Engineering Frank L. Lewis, Professor of Electrical Engineering Kamesh Subbarao, Associate Prof. of Mechanical and Aerospace Engineering DARPA Robotics Challenge 2012 Track B Team: RE2, Inc., UT Arlington, Soar Technologies, Inc.
DRC Synopsys Connection with National Robotics Intiative (NRI) DoD Disaster Relief Scenario inspired by Katrina, Gulf Spill and Fukushima – robotic solution without using custom fixtures and tooling, aims to increase the number of innovators Scenario (time limits under 1 hour for each event) 1. Drive a utility vehicle at the site (no door, but regular vehicle) 2. Travel dismounted across rubble (something a human can do) 3. Remove debris blocking an entryway (up to 5kg boulders) 4. Open a door and enter a building (open door by handle) 5. Climb an industrial ladder and traverse an industrial walkway (ladder goes all the way to floor, therefore no arm pull-up strength required) 6. Use a tool to break through a concrete panel (human tools like jackhammer) 7. Locate and close a valve near a leaking pipe 8. Replace a component such as a cooling pump
DRC Technology Human - Predictive Models – Supervised Autonomy – Robot - Environment Questions to address: -What kind of I/O from human to robots or virtual robot models -What kind of messages to robot over degraded communication links -What kind of autonomy/mobility/manipulation/energy algorithms for robot. Human Factors Leading to DARPA Project: -Disaster environments, even degraded, have been engineered for humans. -No shortage of human tools (vehicles, power tools) that should be useable by robots -No expert roboticists required to operate robots (operation in natural way) – therefore some human intiution for robots is required Gazebo Simulation Environment – Open Source Robotics FoundationGazebo Simulation Environment Atlas Petman GFE – Boston DynamicsAtlas Petman GFE
Lecture 1: Intro to Robotics Outline: –Origins of robotics in the scifi artistic genre –Definition of robots –Manipulators and mobile robots –History of robotics with timeline –Overview of robotics research at ARRI-UTA –Basic robotics concepts
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.
History of Robotics Should robots look like humans? “anthropomorphic or humanoid robots”. Need for these machines to also be intelligent - link to “Artificial Intelligence (AI)”. Need for humans to create machines similar to them is rooted in religious beliefs, recommended reading “God in the Machine” by Anne Foerst It is not the appearance of the robot that most connects it to humans: HAL in “Space Odyssey 2001”, Lt. Data in “Startrek-TNG”, R2D2 and C3PO in “Star Wars”. Which one is more “likeable” and why?
History of Robotics Robots need not look like humanoids, but they make use of: –Strong & precise articulated arms to accomplish tasks that were performed by humans – “articulated robots”, or “manipulators”. Fear that they will replace human laborers. –Use of mobility to reposition the robot from one location to another, “mobile robots”. This can be done by locomotion like humans do (“legged robots”), but most likely it will use other means such as wheels (“wheeled robots”). Robotics is a multi-disciplinary field. Best robotics researchers and engineers will touch upon all disciplines: –Mechanical Engineering – concerned primarily with manipulator/mobile robot design, kinematics, dynamics, compliance and actuation. –Electrical Engineering – concerned primarily with robot actuation, electronic interfacing to computers and sensors, and control algorithms. –Computer Science – concerned primarily with robot programming, planning, and intelligent behavior.
Definition of Robots According to the Robotics Industries Association (RIA): “A robot is a reprogrammable multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks (Jablonski and Posey, 1985)”. This definition underscored the reprogrammability of robots, but it also just deals with manipulators and excludes mobile robots. Close relationship with the concept of “automation”, the discipline that implements principles of control in specialized hardware. Three levels of implementation: –Rigid automation – factory context oriented to the mass manufacturing of products of the same type. Uses fixed operational sequences that cannot be altered. –Programmable automation – factory context oriented to low-medium batches of different types of products. A programmable system allows for changing of manufacturing sequences. –Flexible automation – evolution of programmable automation by allowing the quick reconfiguration and reprogramming of the sequence of operation. Flexible automation is often implemented as “Flexible robotic workcells” (Decelle 1988, Pugh 1983). Reprogramming/retooling the robots changes the functionality of the workcell.
Definition of Robots According to the Japanese Industrial Robot Association (JIRA), robots can be classified as follows: –Class 1: manual handling device – a device with several DOF’s actuated by the operator. –Class 2: fixed sequence robot – similar to fixed automation. –Class 3: variable sequence robot – similar to programmable automation. –Class 4: playback robot – the human performs tasks manually to teach the robot what trajectories to follow. –Class 5: numerical control robot – the operator provides the robot with the sequence of tasks to follow rather than teach it. –Class 6: intelligent robot – a robot with the means to understand its environment, and the ability to successfully complete a task despite changes in the surrounding conditions where it is performed. Another definition describes robotics as the intelligent connection between perception and action (Brady 1985). This is an overly inclusive definition. Yet another definition, which focuses on mobile robots (Arkin 1998) is “A robot is a machine able to extract information from its environment, and use this knowledge to move safely, in a meaningful and purposive manner”.
Definition of Robots G. Bekey definition: an entity that can sense, think and act. Extensions: communicate, imitate, collaborate Classification: manipulators, mobile robots, mobile manipulators. SenseThinkAct Robot
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 – 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 CNC machine tools for accurate milling of aircraft parts are introduced – 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” – 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 Joseph Engleberger, a Columbia physics student buys the rights to Devol’s robot and founds the Unimation Company – 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.
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) – Kawasaki Heavy Industries in Japan acquires a license for Unimate – 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 – 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) – First snake-like robot – ACM III – Hirose – Tokyo Inst. Of Tech – 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)
Snake (MIT) and Swimming (Eel) Robot (UHK)
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) – 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 – 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.
KUKA They can load, unload, deburr, flame-machine, laser, weld, bond, assemble, inspect, and sort.
IBM 7535 IBM 7535 Manufacturing System provided it advanced programming functions, including data communications, programmable speed.
Nomad mobile robot The XR4000 is an advanced mobile robot system that incorporates state of the art drive, control, networking, power management, sensing, communication and software development technologies.
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, Khypera, 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.
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.
Cog – MIT AI Lab Cog is a humanoid robot. It has a torso, arms and a head but no legs. Cog's torso does not have a spine but it can bend at the waist from side-to-side and from front-to-back and can twist its torso the same way a person can. Cog's arms also move in a natural way.
Hierarchical family of robots (K- Team - Switzerland) Koala (20 in) Khepera (6 in) Alice (1 in)
ARV Wall-Climbing Robot for Fuselage Inspection
Robot History Timeline 2000’s – IRobot introduces the first autonomous vacuum – “Roomba”. 2000’s – Mini and micro robots, “Smart Dust” – 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 Robotics 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 and needed to be processed to command Actuators output Microsoft Robotics Studio provide a programmatic way to create an orchestrator that manage robotics applications (“Service”) Inputs Actuators Multiple Sensory Inputs Multiple Actuator Outputs Orchestrator 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.”
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 (UMN) Scout & Ranger Series
The Humanoids are Coming
Human-Machine Interaction Research Study of physiological effects of a robot on a human. Measurements taken Heart Rate (electrocardiograph) Respiration Perspiration Pulse Wave Blood Pressure They used the above data to analyze the emotional state of the human participated in the experiment. They also have a 12 DOFs serial mechanism to capture a human motion so that they could incorporate motion with the data from the measurements. The experiment was performed by putting a human wearing all the measuring devices in the working environment with a robot to have interactions with humanoid robot and they interpreted all the data from measurements to indicate the participant’s stress level. K. Itoh, H. Miwa, Y. Nukariya, M. Zecca, H. Takanobu, S. Roccella, M. C. Carrozza, P. Dario and A. Takanishi, Development of a Bioinstrumentation System in the Interaction Between a Human and a Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems October 2006.
Social Interactive Robot Research This research proposed a facial expression imitation system that consists of two part; facial expression recognition and facial express generation through a robot. The facial expression part is able to classify basic facial expressions includes; neutral, happiness, sadness, anger, surprise, disgust and fear. S. Sosnowski, A. Bitterman, K. Kuhnlenz, and M. Buss, Design and Evaluation of Emotion Display EDDIE. IEEE/RSJ International Conference on Intelligent Robots and Systems October 2006.
Research on Dynamics of Humanoid Robots Whole body motion is very complicated, so when a humanoid robot is to be controlled remotely through teleoperation it is more intuitive to use marionette device to control the humanoid robot T. Takubo, K. Inoue and T. Arai, Wholebody Teleoperation for Humanoid Robot by Marionette System. IEEE/RSJ International Conference on Intelligent Robots and Systems October 2006.
Research on Human-Robot Interaction Haptic Devices Robot Assisted Minimal Invasive Surgery Parallel/Redundant Haptic Devices Multi-Fingered Haptic Interface
Robotics Applications Today, commercial robots are used routinely in the following applications: –Industrial Manufacturing – “Transforming objects” - arc/spot welding, milling/drilling, glueing/sealing, laser/water jet cutting, grinding, deburring, screwing, painting, and assembly. –Material Handling: “Pick and Place”- palletizing (placing objects on a pellet in an ordered way), warehouse loading/unloading, part sorting, packaging, electronic chip pick and place, hazardous material handling. –Measurement: object finding, contour finding, inspection, 3D registration. –Entertainment robotics: animated figures, flight simulator, robotic pets. –Service robotics: robotic aids for handicapped people, artificial limbs, robotic vacuum, courier. –Military robotics: defusing explosive devices, scout robots, UAVs. –Surgical Robotics: drilling, suturing, cauterizing, tool holding.
Robotics Applications Robot prices continue to drop compared to the cost of human labor. In the year 2000, 78% of all robots installed in the US were welding or material-handling robots.