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Intro UVS Popa

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UVS
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Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014 XX4378/5378 Introduction to Unmanned Vehicle Systems Instructor: Dan Popa, Ph.D., Associate Professor, EE Office hours: Tue-Thu 12:30am – 2:30pm, NH543 Email: [email protected] , Website: http://ngs.uta.edu Today’s Lecture A few UVS case Studies Related fields: Systems and Controls, Automation Science, Robotics Robotics vs Unmanned Vehicles Jargon and Basic Concepts Examples from NGS and UTA Competitions
Transcript
Behavior-Based RoboticsXX4378/5378 Introduction to Unmanned Vehicle Systems
Instructor: Dan Popa, Ph.D., Associate Professor, EE
Office hours: Tue-Thu 12:30am – 2:30pm, NH543
Email: [email protected] , Website: http://ngs.uta.edu
Related fields:
Robotics vs Unmanned Vehicles
Jargon and Basic Concepts
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
UVS Technology Areas
(Prof. Brian Huff)
Demonstration toward IHS (Intelligent Highway System) in California started in 2003 (Bus Platoons).
Benefits
Reduced accidents
Reduced operating and maintenance costs
Smoother ride and level boarding for faster travel and reduced dwell time
“Rail-like” status
Encourage transit oriented development
Field tests in 2012.
Wei-Bin Zhang’s full presentation at this link
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
VAA field testing addressed deployment issues and benefits/costs through revenue-service operation
California-Oregon team selected magnetic guidance, rather than vision as the primary guidance technology based on thorough evaluation and technical merits
Full range of VAA applications were field tested.
Highway and urban application
Precision docking and guidance
Case Study: Vehicle Assist and Automation Technologies (VAA Project) Wei-Bin Zhang, UC Berkeley
Wei-Bin Zhang’s full presentation at this link
*
Magnetic Marker System
*
Automated Bus (New Flyer, 60’)
VAA System Installation/Configuration
Indicators (2 sets)
Yaw rate gyro
Control computer (2)
VAA Test (Oregon): Lane Transit District
Precision Docking + Lane Guidance
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
VAA Field Testing
Reading: “ Development and Validation of an Automated Steering Control System for Bus Revenue Service, “Han-Shue Tan, Jihua Huang, and Wei-Bin Zhang , in Proc. Of IEEE CASE Conference, August 2014.
(best application award winner)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Other Notable Projects
Korea Autonomous Vehicle Project
CMU SRX Self-Driving Vehicle and Boss (DARPA Urban Challenge) 2007
Stanford Stanley (DARPA Grand Challenge) 2005
Paper reference: C.C. Eriksen et. al., Seaglider: “A Long-Range Autonomous Underwater Vehicle for Oceanographic Research,” in IEEE Journal of Oceanic Engineering, 2001.
ARCAS – unmanned flying and cooperative manipulation – 2012
http://www.arcas-project.eu/multimedia
Related Disciplines to UVS
Automation Science and Engineering
System: Object in which input signals interact to produce output signals.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Systems Thinking:
Intellectual Themes
Manage complexity by reusing simple components (electrical, mechanical or code) nomenclature: devices
Abstraction (ex: system)
Block diagrams, Data flow diagrams (state machines), signal flow graphs, electric circuits
Modeling (ex: I/O models)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
System Classification
If U →Y, U1 →Y1, U2 →Y2 then
U1+U2 → Y1+Y2
A*U →A*Y
Nonlinear systems do not have this property, and the I/O map is represented by a nonlinear mapping.
Examples: Diode, Dry Friction, Robot Arm at High Speeds.
Memoryless vs. Dynamical
A memoryless system is represented by a static (non-time dependent) I/O map: Y=f(U).
Example: Amplifier – Y=A*U, A- amplification factor.
A dynamical system is represented by a time-dependent I/O map, usually a differential equation:
Example: dY/dt=A*u, Integrator with Gain A.
Mandelbrot set, a fractal image, result of a Nonlinear Discrete System Zn+1=Zn²+C
Exact Equation, nonlinear
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
System Classification
Time-Invariant vs. Time Varying
Time-invariant system parameters do not change over time. Example: pendulum, low power circuit
Time-varying systems perform differently over time. Example: human body during exercise.
Causal vs. Non-Causal
For a causal system, outputs depend on past inputs but not future inputs. Examples: most engineered and natural systems
A non-causal system, outputs depend on future inputs. Example: computer simulation where we know the inputs a-priori, digital filter with known images or signals.
Stable vs. Unstable
For a stable system the output to bounded inputs is also bounded. Example: pendulum at bottom equilibrium
For an unstable system the ouput diverges to infinity or to values causing permanent damage. Example: short circuit on AC line.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
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
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Types of Models
Black Box
Example: regression (data fit)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
RLC Circuit as a System
Kirchoff’s Voltage Law (KVL):
*
White Box Systems: Mechanical
Newton’s Law:
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White-Box vs. Black-Box Models
Grey-Box Models
White Box vs Black Box Models
Start to understand simple white continuous time models which are linear
Eventually deal with grey-box or black-box models in real-life
White Box Models
Good Extrapolation Good understanding High reliability, scalability
Short time to develop Little domain expertise required Works for not well understood systems
Disadvantages
Not scalable, data restricts accuracy, no system understanding
Application Areas
Complex processes Existing systems
Diagram Representation of Systems
System Simulation Software
Matlab and Simulink
System Diagrams
Helps understand flow of information (signals) through a complex system
Helps visualize I/O dependencies
Based on a set of primitives:
Transfer Function Summer/Difference Pick-off point
Signal Flow Graph (SFG):
Block Diagram Simplification Rules
Control System Block Diagram
Key Transfer Functions
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Jlh: Can we make the diagram bigger and the title smaller?
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
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)
Classical Period of AC: driven by communication and aircraft control applications
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Modern Period of AC: driven by space exploration
Time domain analysis (state-space)
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Feedback Control
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
Transient Response Characteristics
Basic Control Actions: u(t)
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Jlh: Can we give more intuition on control actions. This seems real brief considering its importance.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Control Systems: Summary
Abstraction is the basis for system level thinking. Abstraction requires advanced mathematics, and is required of 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.
In particular in UVS
Robots as Complex Systems Controlled by Feedback
G. Bekey definition: an entity that can sense, think and act.
Extensions: communicate, imitate, collaborate
Sense
Think
Act
Robot
Robots and Robot Science
Robots make use of:
Strong & precise articulated arms to accomplish tasks that were performed by humans – “articulated robots”, or “manipulators”.
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.
Industrial Engineering – concerned with manufacturing and logistics, utilization, efficiency, reliability of robots.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Robot System Architectures
Advantage: optimization of resources and actions is possible.
Drawbacks: internal model maintenance/update consumes resources. This model may have problems with long reaction times, inapropriate decisions and deadlock.
Work-around: put a human operator in the loop.
2) Behavioral (reactive). Sense-Think-Act cycle is decentralized in parallel mode. Brooks proposes a subsumption architecture with 8 behaviors: reason about objects, plan changes to world, identify objects, monitor changes, build maps, explore, wander, avoid objects.
Advantages: quick reaction, multiple goals, no conflict resolution needs, easy to extend, debug, etc.
Disadvantages: sub-optimal, not clear how to describe and implement complex plans.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
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.
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
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”.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Mobile Robots vs. UVS
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.
Unmanned Vehicle System Definition:
System that uses its mobility to deliver a payload at a remote location in any environment (water, land, air, space).
Unlike a robot, it could also be remotely piloted by a human operator
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Relationship with Automation
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.
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)”.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Automation: Manufacturing (not related to UVS)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Automation: Buildings (not related to UVS)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Automation: Pilot Assistance (UVS Relevance)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Jan 2013: Amazon Prime Air Delivery (UVS Relevance)
Editorials in IEEE Transactions of Automation Science and Engineering
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
UVS and Automation
Automation Science is concerned with reliability, repeatability, scalability, logistics and efficiency of systems, in particular UVS.
Analogy: Put one man on the moon=robotics, put 100 men on the moon=automation.
UVS operations need to consider automation concepts: fault tolerance, optimization, etc.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
System of Systems:
SMSS (Lockheed Martin)
Robot History Timeline
Relevant to UVS
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.
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
G. Walter Grey's tortoise
These vehicles had a light sensor, touch sensor, propulsion motor, steering motor, and a two vacuum tube analog computer.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
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 – 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.
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Snake-like robot
Robot History Timeline
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, later to become Boston Dynamics technology.
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Nomad mobile robot
The XR4000 (circa 1990) was an advanced mobile robot system that incorporates state of the art drive, control, networking, power management, sensing, communication and software development technologies.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Rensselaer Polytechnic Institute
(Sanderson & Lee)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
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 –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), first service robots (as couriers in hospitals, etc)
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Asimo
               
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 – Robotic Deployment of Mobile Sensor Networks
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
USC Mobile Robots
Flying Insect
Solar AUV
Richard Blidberg
Hierarchical family of scouting robots
(Nikos Papanikolopoulos, Univ. of Minnesota)
Scout & Ranger Series
Robot Subsystems
A mechanical structure.
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, and/or a remote control.
We describe robots 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, many UVS have fewer than 6 DOF.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Locomotion Mechanisms
Mobile Manipulators
Properties of Manipulators
(Can extend to UVS)
The most important considerations for the application of an industrial robot are:
Manipulator performance
System integration
Manipulator performance is defined as:
Reach (size of workspace), and dexterity (angular displacement of individual joints). Some robots can have unuseable workspace due to dead-zones, singular poses, wrist-wrap poses.
Payload (weight that can be carried). Inertial loading for rotational wrist axes can be specified for extreme velocity and reach conditions.
Quickness (how fast it can move). Critical in determining robot throughput but rarely specified. Maximum speeds of joints are usually specified, but average speeds while carrying payloads in a working cycle is of interest.
Duty-cycle (how fast it can repeat motions without breaking down).
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Properties of Manipulators
(Can Extend to UVS)
Precision is defined by using 3 metrics: resolution, repeatability and accuracy.
These concepts are usually static, and dynamic precision is usually not specified.
Accuracy is defined as how close the manipulator can come to a given point within its workspace.
Accuracy varies with the location of the point
Repeatability is how close the manipulator returns to the same point in space.
Most present day manipulators are highly repeatable but not very accurate.
Repeatability for the manipulator is also defined as the ability to return to a so called “taught” position.
Resolution is defined as the minimum motion increment that the manipulator can perform and detect.
example: a robot controller has 12-bit storage capacity, the full range of the robot = 1.0 cm for one joint
spatial resolution = 1.0cm/212 = 1.0 cm/4096 = 2.44 µm
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Resolution: The smallest output increment that a machine can perform.
Repeatability: The ability of a machine to return to the same state over many cycling attempts.
Accuracy: The maximum expected difference between the actual and the ideal (desired) output for a given input.
In Traditional Metrology, Resolution and Accuracy are mean values, while Repeatability is a statistical distribution.
good accuracy
poor repeatability
poor accuracy
good repeatability
good accuracy
good repeatability
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 moving object. 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 vehicle is called the body frame.
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 a system equals the number of independent position variables that would have to be specified in order to locate all its moving parts.
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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Basic Concepts
Dynamics is devoted to studying the forces required to cause motion.
The relationship between the actuator forces and 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.
Kinematics is usually sufficient if the robot is gravity compensated and moves at slow speeds.
Dynamics is necessary for accurate 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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Basic Concepts
Robot 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.
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Research at Next Gen Systems (NGS)
Dan Popa
ARRIpede: a Millimetric Microcrawler
Weight ~ 4g (with battery)
Repeatability better than 12 μm
Continous operation: 10 minutes at max speed, 100 minutes at .1x max speed
*
Human Robot Interaction
Physical HRI
Algorithms for Deployment of Networked Robots
Sensor deployment algorithms with mobile robots:
Dynamic sensor repositioning using potential fields (PF) in the presence of network communication constraints (Popa & Stephanou 2004-2006).
Optimal movement of mobile sensors to gather the most information based on closed-form and EKF covariance of parameter estimates ([Popa & Sanderson, 2004-2006]
Resource coordination to accomplish a common mission using a Discrete Event Controller (DEC) ([Lewis & Popa 2005-2006].
Low-Cost In-Door Mobile Platforms for Validation:
ARRI Bots, Garcia Acroname, LynxMotion Spiders and Cyberguard Sentry.
A matrix – based discrete event controller (DEC) is a convenient way to describe a monitoring mission.
[Lewis 2006]
navigation and localization uncertainty
with distance between sensors
y
Program Missions- Selection of matrices
Node Deployment & Failure- Modify Fr
RESOURCE RESET LOGIC:
Recent/Current NGS Projects
Robots using human tools, learning and adaptation skills
Robots who Live Among Us – National Robotics Initiative
Co-Robots with sensitive skin and adaptive physical HRI - NSF
Service Robots - Shared Control, Intuitive Man-Machine Interfaces – Qinetiq - NA
Helping people with disabilities – Assistive Robotics Consortium
– Treating people with cognitive disabilities – Social Robots – This project
– Treating people with motor disabilities – Rehabilitation – NSF
- Manufacturing Robots -
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UTA Student Competition List (2013)
Competition
Type
Schedule
Ground (AUVSI)
11th Annual Student Unmanned Air Systems (SUAS) Competition (Undergraduates + 1Graduate)
Aerial (AUVSI)
Webster Field, Patuxent River, MD
6th International RoboBoat Competition
16th International RoboSub Competition
DARPA Robotics Challenge
Kick-off October 26, 2012
Ground (IEEE)
Denver, Colorado
Microscale (IEEE)
Ground (IEEE)
Karlsruhe, Germany
Ground (NASA)
NASA, Johnson Space Center, Houston, TX
*
Project Teams (2013)
Student leader: Hakki Sevil, Onur Daskiran
RoboBoat Team: (Dr. Huff)
Student leader: Nahum Torres
Student leader: Isura Ranatunga
Student leaders: Isura Ranatunga, Ghassan Atmeh
NASA RASC-AL Exploration Robo-Ops Team: Dr. Bowling
Student leaders: Adrian Rodriguez
NIST/IEEE Mobile Microrobot Challenge
*
Karlsruhe, 2013
Dan O. Popa, “Robust and Reliable Microtechnology Research and Education Through the Mobile Microrobotics Challenge,” in IEEE R&A Magazine, March 2014.
Hong Kong, 2014
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Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Dan O. Popa, Ph.D., Associate Prof. of Electrical Engineering
Timeframe: 27 Months (Phase I 15 mo (Ia – 9 mo, Ib - 6 mo), Phase II 12 mo)
DARPA Robotics Challenge 2012
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DRC Team Members
 
VP R&D, RE2 Engineer, RE2 Engineer, RE2
UTA DRC Team
Dr. Dan Popa, UTA project lead Dr. Alan Bowling Dr. Gian Luca Mariottini Dr. Frank Lewis Dr. Kamesh Subbarao Isura Ranatunga Adrian Rodrigues Aaron Staranowicz Ghassan Atmeh Suresh Sampathkumar Ishan Chakravorty
EE Faculty MAE Faculty CSE Faculty EE Faculty MAE Faculty EE Ph.D. student MAE Ph.D. student CSE Ph.D. student MAE Ph.D. student EE M.S. Student EE UG Student
SoarTech
Drive a utility vehicle to the site
no door, John Deere Gator
Travel dismounted across rubble
Open a door and enter a building
open door using handle
Climb an industrial ladder
traverse an industrial walkway
human tools like jackhammer)
Connect a hose
DRCSim and DRC Robots
Enables accurate robot simulation with good hardware/software interchangeability
Supports common robot control software such as ROS
Dan O. Popa, XX4378/5378 Intro to UVS, Fall 2014
Tasks – UTA Team
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Select Resources- Priority modification of F
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