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					Introduction to ROBOTICS


    Mobot: Mobile Robot




                           1
            Course Topics
• Introduction
• Mobile Robot Locomotion
   – Classification of wheels
• Mobile Robot Kinematics
• Perception
• Mobile Robot Localization
• Planning and Navigation
   Autonomous Mobile Robots
• Three key question in Mobile Robotics
 – Where am I?
 – Where to go?
 – How to get there?
                              ?
   Autonomous Mobile Robots
• To answer these questions the robot has
  to
 – have a model of the environment (given or
   autonomously built)
 – perceive and analyze the environment
 – find its position within the environment
 – plan and execute the movement
From Manipulators to Mobile Robots
      Applications of Mobile Robots
            Indoor                                  Outdoor
   Structured Environments                 Unstructured Environments


                                                                   mining
     transportation
   industry & service                       space              sewage tubes

customer support                                         agriculture
museums, shops ..                          forest
                          cleaning ..
                        large buildings                                air
     research,                                      construction
  entertainment,            surveillance    demining
        toy                  buildings                             underwater
                                             fire fighting
                                                               military
Automatic Guided Vehicles
         Newest generation of Automatic Guided
         Vehicle of VOLVO used to transport motor
         blocks from on assembly station to an other.
         It is guided by an electrical wire installed in
         the floor but it is also able to leave the wire
         to avoid obstacles. There are over 4000 AGV
         only at VOLVO’s plants.
Helpmate




 HELPMATE is a mobile robot used in
 hospitals for transportation tasks. It has
 various on board sensors for autonomous
 navigation in the corridors. The main sensor
 for localization is a camera looking to the
 ceiling. It can detect the lamps on the ceiling
 as reference (landmark).
 http://www.ntplx.net/~helpmate/
          BR700 Cleaning Robot




BR 700 cleaning robot
developed and sold by
Kärcher Inc., Germany. Its
navigation system is based
on a very sophisticated
sonar system and a gyro.
http://www.kaercher.de
ROV Tiburon Underwater Robot




                Picture of robot ROV Tiburon
                for underwater archaeology
                (teleoperated)- used by MBARI
                for deep-sea research, this UAV
                provides autonomous hovering
                capabilities for the human
                operator.
The Pioneer




    Picture of Pioneer, the teleoperated
    robot that is supposed to explore
    the Sarcophagus at Chernobyl
                         The Pioneer




PIONEER 1 is a modular mobile robot offering various options like a gripper
or an on board camera. It is equipped with a sophisticated navigation library
developed at Stanford Research Institute (SRI).
http://www.activmedia.com/robots
The B21 Robot


            B21 of Real World
            Interface is a
            sophisticated mobile
            robot with up to three
            Intel Pentium
            processors on board. It
            has all different kinds
            of on board sensors for
            high performance
            navigation tasks.
            http://www.rwii.com
               The Khepera Robot




KHEPERA is a small mobile robot for research and education. It sizes only
about 60 mm in diameter. Additional modules with cameras, grippers and
much more are available. More then 700 units have already been sold (end
of 1998). http://diwww.epfl.ch/lami/robots/K-family/ K-Team.html
Forester Robot


             Pulstech developed
             the first ‘industrial like’
             walking robot. It is
             designed moving
             wood out of the forest.
             The leg coordination is
             automated, but
             navigation is still done
             by the human
             operator on the robot.
             http://www.plustech.fi
Robots for Tube Inspection
              HÄCHER robots for sewage tube
              inspection and reparation. These
              systems are still fully teleoperated.
              http://www.haechler.ch




              EPFL / SEDIREP: Ventilation
              inspection robot
   Sojourner, First Robot on Mars




The mobile robot Sojourner was used during the Pathfinder mission to
explore the mars in summer 1997. It was nearly fully teleoperated from
earth. However, some on board sensors allowed for obstacle detection.
http://ranier.oact.hq.nasa.gov/telerobotics_page/telerobotics.shtm
NOMAD, CMU/NASA
The Honda Walking Robot Asimo




   http://www.honda.co.jp/tech/other/robot.html
    Toy Robot Aibo from Sony
• Size
 – Length about 25 cm
• Sensors
 – Color camera
 – Stereo microphone
Control Scheme for M.R. Systems
       Knowledge,                               Mission
        Data Base                              Commands



              Localization       "Position"       Cognition
              Map Building       Global Map      Path Planning


             Environment Model                      Path
                 Local Map

               Information                          Path




                                                                   Motion Control
                Extraction                        Execution
Perception




                Raw data                       Actuator Commands


                 Sensing                           Acting


                                  Real World
                                 Environment
Control Architectures/Strategies
• Control Loop
 – Dynamically changing
 – No compact model available
 – Many sources of uncertainty
• Two approaches
 – Classic AI     Localization        "Position"
                                      Global Map
                                                     Cognition

                 Environment Model                      Path
                     Local Map


 – New AI, AL     Perception          Real World
                                     Environment
                                                   Motion Control
            Two Approaches
• Classical AI
  (model based
  navigation)
 – Complete modeling
 – Function based
 – Horizontal
   decomposition
                Two Approaches
New AI, AL
 (behavior based
 navigation)
 –   Sparse or no modeling
 –   Behavior based
 –   Vertical decomposition
 –   Bottom up
                  Mixed Approach
   • Mixed Approach Depicted into the General
     Control Scheme
Localization            Position                                Cognition
                        Position
                       Local Map
                       Local Map




                                    Perception to




                                                                 Feedback
                                                    Avoidance
                                                                 Position
                                                     Obstacle
                                       Action




                                                                            Path
  Environment Model
      Local Map

                       Real World
                      Environment
Perception                                      Motion Control
 Keys for Autonomous Navigation
• Environment Representation
 – Continuous metric  x, y, 
 – Discrete metric  metric grid
 – Discrete topological  topological grid
 Keys for Autonomous Navigation
• Environment Modeling
 – Raw sensor data, e.g., laser range data, grayscale
   images
   • Large volume of date, low distinctiveness
   • Make use of all acquired information
 – Low level features, e.g., corners, lines, other
   geometric features
   • Medium volume of data, average distinctiveness
   • Filters out the useful information, still ambiguities
 – High level features, e.g., doors, a car, the Eiffel tower
   • Low volume of data, high distinctiveness
   • Filters out the useful information, few/no ambiguities, not
     enough information
   Environment Representation & Modeling
• Environment representation and modeling:
  How to do?      Modified       Feature-based
        Odometry               Environment                Navigation
           39


                121
                      95
           34


                      25                                           Corridor
                                                                   crossing

                                                   Elevator door



                                                                   Entrance
 How to find a treasure
                               Landing at night     Still a challenge for
     Not applicable        Expensive, inflexible     artificial systems
   Environment Representation: Map Category
• Recognizable locations          • Topological maps




• Metric topological maps         • Fully metric maps (cont. or
                                  y
                                    disc.)

                        200 m
    50 km

              2 km


                       100 km


                                                           x
                                {W}
          Environment Models
                          • Raw data
• Continuous
                           – As perceived by sensors
  – Position in x, y, 
                          • Features
                           – Environmental structures
• Discrete                   which are static, always
  – Metric grid              perceptible with the
  – Topological grid         current sensor system
                             and locally unique
                           – e.g., geometric elements
                             (lines, walls, columns, …),
                             a railway station, a river,
                             the Eiffel tower,
                             skyscraper
         Human Navigation
• Topological with imprecise metric
  information
                             ~ 400 m




                                          ~ 1 km

                   ~ 200 m




          ~ 50 m

                                 ~ 10 m
                                                      Approaches with
                                                      Limitations
               Method for Navigation
• Incrementally       • Modifying the environment
  (dead                 (artificial landmarks/beacons)
  reckoning)

                                                 Inductive or optical
                                                 tracks (AGV)




                         Reflectors or
   Odometric or          barcodes
   inertial sensors
   (gyro)
   Not applicable                 Expensive, inflexible
                                                                       The quantitative
                                                                       metric approach
            Methods for Localization
1. A priori map: graph, metric             3. Matching:
                                              Find correspondence
    y                                         of features
   wy              lw
        r               r




 {W}                        wx
                                 r   x

2. Feature extraction                      4. Position estimation (e.g.,
                                              Kalman filter, Markov)

                                              Odometry Observation


                                         • Representation of uncertainties
                                         • Optimal weighting according to
                                           a priori statistics
  Gaining Information through Motion

• Multi-hypotheses tracking

                              Believe state
      Grid-Based Metric Approach
  • Grid map of the Smithsonian Museum of
    American History in Washington D.C.
Grid: ~ 400300 = 128,000 points
                                                                     The quantitative
                                                                     topological approach
                 Methods for Localization
1. A priori map: graph                        3. Library of driving behaviors
                                              e.g., wall or midline following, blind
Locally unique
                                                 step, enter door, application
points                                           specific behaviors. Example: video-
                                                 based navigation with nature
 edges                                           landmarks



2. Method for determining the
   local uniqueness




                         e.g. striking changes on raw data level or highly distinctive features
 Map Building: How to Establish
• By hand

                                           12 3.5




• Automatically: Map Building
 – The robot learns its environment
 – Motivation:
   • By hand: hard and costly
   • Dynamically changing environment
   • Different look due to different perception
 Map Building: How to Establish
• Basic requirements of a map
  – A way to incorporate newly sensed information
    into the existing world model
  – Information and procedures for estimating the
    robot’s position
  – Information to do path planning and other
    navigation task (e.g. obstacle avoidance)
 Map Building: How to Establish
• Measure of quality of a map
  – Topological correctness
  – Metrical correctness
• But: Most environments are a mixture of
  predictable and unpredictable features  hybrid
  approach
  – Model-based vs. behavior-based
        Map Building: The Problems
• Map maintaining: Keep track                 • Representation and reduction
  of changes in the environment                 of uncertainty
                                                – Probability density for feature
  e.g. disappearing                               positions
  cupboard                                      – Additional exploration strategies


                                                 Position of robot  position of wall




                                 ?




                  e.g. measure of belief of
                                                 Position of wall  position of robot
                  each environment feature
   Map Building: Exploration & Graph Construction
• Exploration                       • Graph construction
                         explore
                         on stack
                         already
                         examined




                                      – Where to put the nodes?
  – Provide correct topology            •   Topology-based: at distinctive
                                            locations
  – Must recognize already
    visited locations
                                        •   Metric-based: where features
  – Backtracking for unexplored             disappear or get visible
    openings
                          Control for Mobile Robots
               Knowledge,
               Data Base
                                                         Mission
                                                        Commands
                                                                                             • Most functions
                                                                                               for navigation
                                                                                               are ‘local’ not
                        Localization       "Position"       Cognition                          involving
                        Map Building       Global Map      Path Planning                       localization
global
                                                                                               and cognition
                      Environment Model                       Path
                          Local Map

local
                                                                                             • Localization
                         Information                          Path
                          Extraction                        Execution                          and global




                                                                            Motion Control
                                                                                               path planning
         Perception




                                                                                                slower
                          Raw data                      Actuator Commands                      update rate,
                                                                                               only when
                          Sensing                            Acting                            needed

                                          Real World                                         • This approach
                                          Environment                                          is pretty similar
                                                                                               to what human
                                                                                               beings do
              Demo Videos
• Tour-Guide Robot    • Autonomous Indoor
  (Nourbakhsh, CMU)     Navigation (Thrun,
                        CMU)
Tour-Guide Robot (EPFL@expo.02)
 Autonomous Indoor Navigation
• Pygmalion EPFL
  –   Very robust on-the-fly localization
  –   One of the first systems with probabilistic sensor fusion
  –   47 steps, 78 meter length, realistic office environment
  –   Conducted 16 times > 1 km overall distance
  –   Partially difficult surface (laser), partially few vertical edges
      (vision)
               Demo Videos
• Autonomous Robot for    • Humanoid Robots
  Planetary Exploration     (SONY)
  (ASL – EPFL)
GuideCane (U of Michigan)
 LaserPlans Architectural Tool
• ActiveMedia Robotics
             Demo Videos
• High-Speed
  Exploration and   • Turning Real Reailty
  Mapping             into Virtual Reality
Outdoor Mapping (no GPS)
map (trees) and path




                       University of Sydney
Real-Time Multi-robot Exploration
               Course Topics
• Introduction
• Mobile Robot Locomotion
    – Classification of wheels
•   Mobile Robot Kinematics
•   Perception
•   Mobile Robot Localization
•   Planning and Navigation
                      Locomotion




 Locomotion is the process of causing an autonomous robot to move
     In order to produce motion, forces must be applied to the vehicle

                                                                          53
Wheeled Mobile Robots (WMR)




                              54
      Wheeled Mobile Robots
• Combination of various physical (hardware)
  and computational (software) components
• A collection of subsystems:
  – Locomotion: how the robot moves through its
    environment
  – Sensing: how the robot measures properties of itself
    and its environment
  – Control: how the robot generate physical actions
  – Reasoning: how the robot maps measurements into
    actions
  – Communication: how the robots communicate with
    each other or with an outside operator

                                                           55
       Wheeled Mobile Robots
• Locomotion — the process of causing an robot to move.
   – In order to produce motion, forces must be applied to the robot
   – Motor output, payload

• Kinematics – study of the mathematics of motion
  without considering the forces that affect the motion.
   – Deals with the geometric relationships that govern the system
   – Deals with the relationship between control parameters and the
     behavior of a system.

• Dynamics – study of motion in which these forces are
  modeled
   – Deals with the relationship between force and motions.



                                                                       56
Notation

    Posture: position(x, y)
    and orientation 




                              57
Wheels


         Rolling motion




           Lateral slip




                          58
               Steered Wheel
• Steered wheel
  – The orientation of the rotation axis can be controlled




                                                             59
       Idealized Rolling Wheel
                                • Assumptions
                                1. The robot is built from rigid
                                     mechanisms.
                                2.   No slip occurs in the orthogonal
                                     direction of rolling (non-slipping).
                                3.   No translational slip occurs
                                     between the wheel and the floor
                                     (pure rolling).
                                4.   The robot contains at most one
                                     steering link per wheel.
Non-slipping and pure rolling
                                5.   All steering axes are
                                     perpendicular to the floor.



                                                                            60
     Robot wheel parameters
• For low velocities, rolling is a reasonable
  wheel model.
  – This is the model that will be considered in the
    kinematics models of WMR
• Wheel parameters:
  – r = wheel radius
  – v = wheel linear velocity
  – w = wheel angular velocity
  – t = steering velocity

                                                       61
                    Wheel Types
    Fixed wheel                 Centered orientable wheel




Off-centered orientable wheel
(Castor wheel)
                                 Swedish wheel:omnidirectional
                                 property




                                                                 62
                 Fixed wheel
– Velocity of point P

        where,   ax   : A unit vector to X
        axis
– Restriction to the robot mobility
         Point P cannot move to the direction perpendicular to plane of
  the wheel.
                                             x




                                y




                                                                          63
Centered orientable wheels
– Velocity of point P



           where,
               axis
                        ax   : A unit vector of x

               axis
                        ay   : A unit vector of y
– Restriction to the robot mobility




                                                    x




                                              y


                                                        64
Off-Centered Orientable Wheels

– Velocity of point P


           where,
              axis
                     ax   : A unit vector of x

              axis
                     ay   : A unit vector of y
– Restriction to the robot mobility




                                                 x




                                      y


                                                     65
           Swedish wheel
– Velocity of point P


         where,   ax : A unit vector of x axis
                  as : A unit vector to the motion of
                   roller

– Omnidirectional property




                                                x




                                     y


                                                        66
          Examples of WMR
Example                 •   Smooth motion
                        •   Risk of slipping
                        •   Some times use roller-ball
      Bi-wheel type         to make balance
         robot          •   Exact straight motion
                        •   Robust to slipping
                        •   Inexact modeling of
                            turning
     Caterpillar type
        robot           •   Free motion
                        •   Complex structure
                        •   Weakness of the frame
     Omnidirectional
       robot
                                                         67
  Mobile Robot Locomotion
• Instantaneous center of rotation (ICR)
  or Instantaneous center of curvature
  (ICC)
  – A cross point of all axes of the wheels




                                              68
       Degree of Mobility
• Degree of mobility
  The degree of freedom of the robot motion


         Cannot move                    Fixed arc
         anywhere (No                   motion (Only
         ICR)                           one ICR)
• Degree of mobility :     • Degree of mobility : 1
  0
                                         Fully free motion
         Variable arc
         motion (line of                ( ICR can be
         ICRs)                          located at any
• Degree of mobility :                  position)
                           • Degree of mobility :
  2                          3

                                                         69
     Degree of Steerability
• Degree of steerability
    The number of centered orientable wheels
      that can be steered independently in order
      to steer the robot
                                   No centered orientable
                                   wheels

                • Degree of steerability : 0

              One centered
              orientable wheel

              Two mutually                      Two mutually
              dependent                         independent
              centered                          centered
              orientable wheels                 orientable
                                                wheels
• Degree of steerability :        • Degree of
  1                                 steerability : 2           70
    Degree of Maneuverability
• The overall degrees of freedom that a robot can
  manipulate: M     m s
                      
     Degree of Mobility        3      2      2      1      1
     Degree of Steerability     0      0      1      1
       2
•   Examples of robot types (degree of mobility, degree of
    steerability)




                                                               71
Degree of Maneuverability

   M  m  s




                            72
         Non-holonomic constraint
    A non-holonomic constraint is a constraint on the
    feasible velocities of a body

    So what does that mean?
          Your robot can move in some directions (forward
    and backward), but not others (sideward).
The robot can instantly
move forward and backward,
but can not move sideward                                Parallel parking,
                                                        Series of maneuvers




                                                                              73
      Mobile Robot Locomotion
• Differential Drive
   – two driving wheels (plus roller-ball for balance)
   – simplest drive mechanism
   – sensitive to the relative velocity of the two wheels (small error
     result in different trajectories, not just speed)
• Steered wheels (tricycle, bicycles, wagon)
   – Steering wheel + rear wheels
   – cannot turn 90º
   – limited radius of curvature
• Synchronous Drive
• Omni-directional
• Car Drive (Ackerman Steering)
                                                                         74
            Course Topics
• Introduction
• Mobile Robot Locomotion
   – Classification of wheels
• Mobile Robot Kinematics
• Perception
• Mobile Robot Localization
• Planning and Navigation
              Differential Drive


                                                     




• Posture of the                       • Control input
  robot
         (x,y) : Position of the                  v : Linear velocity of the
             robot                                    robot
                : Orientation of the              w : Angular velocity of the
             robot                                   robot
                                                  (notice: not for each wheel)
                                                                                 76
              Differential Drive
VR (t ) – linear velocity of right wheel
VL (t ) – linear velocity of left wheel
r – nominal radius of each wheel
R – instantaneous curvature radius of the robot trajectory
(distance from ICC to the midpoint between the two wheels).


                                 Property: At each time instant, the
                                 left and right wheels must follow a
                                 trajectory that moves around the
                                 ICC at the same angular rate , i.e.,
                                           L                 L
                                  ( R  )  VR       ( R  )  VL
                                           2                 2



                                                                         77
              Differential Drive
Posture Kinematics Model: Kinematics model in world frame
 • Relation between the control input and speed
    of wheels


 • Kinematic equation

                                             
                                                 90  



  • Nonholonomic
    Constraint          x
                         
     sin      cos    x sin   y cos  0
                                      
                         
                        y
                                Physical Meaning?
                                                            78
          Differential Drive
Kinematics model in robot frame
---configuration kinematics model




                                    79
         Basic Motion Control
• Instantaneous center of
  rotation


                              R : Radius of rotation

                   • Straight motion
                         R = Infinity           VR =
                        VL
                   • Rotational motion
                          R= 0                 VR
                        = -VL
                                                       80
       Basic Motion Control
• Velocity
  Profile




                                     3   0   2   1


                         3   0   2   1

                    : Radius of
                    rotation
                    : Length of path
                    : Angle of rotation
                                                     81
                          Tricycle
• Three wheels and odometers on the two rear wheels
• Steering and power are provided through the front wheel
• control variables:
   – steering direction α(t)
   – angular velocity of steering wheel ws(t)


                                                The ICC must lie on
                                                the line that passes
                                                through, and is
                                                perpendicular to, the
                                                fixed rear wheels


                                                                        82
                   Tricycle
• If the steering wheel is
  set to an angle α(t)
  from the straight-line
  direction, the tricycle
  will rotate with angular
  velocity ω(t) about
  ICC lying a distance R
  along the line
  perpendicular to and
  passing through the
  rear wheels.
                              83
Tricycle




    d: distance from the front wheel to the rear axle


                                                   84
                      Tricycle
Kinematics model in the robot frame
---configuration kinematics model




                                      85
                  Tricycle
Kinematics model in the world frame
---Posture kinematics model




                                      86
         Synchronous Drive
• In a synchronous drive robot (synchronous
  drive) each wheel is capable of being
  driven and steered.
• Typical configurations
  – Three steered wheels arranged as vertices of
    an equilateral
  – triangle often surmounted by a cylindrical
    platform
  – All the wheels turn and drive in unison
• This leads to a holonomic behavior
                                                   87
Synchronous Drive




                    88
             Synchronous Drive
• All the wheels turn in unison
• All of the three wheels point in the same direction
  and turn at the same rate
   – This is typically achieved through the use of a complex collection
     of belts that physically link the wheels together
   – Two independent motors, one rolls all wheels forward, one rotate
     them for turning
• The vehicle controls the direction in which the
  wheels point and the rate at which they roll
• Because all the wheels remain parallel the synchro
  drive always rotate about the center of the robot
• The synchro drive robot has the ability to control the
  orientation θ of their pose directly.

                                                                          89
            Synchronous Drive
• Control variables (independent)
  – v(t), ω(t)




                                    90
           Synchronous Drive
• Particular cases:
  – v(t)=0, w(t)=w during a
    time interval ∆t, The
    robot rotates in place
    by an amount w ∆t .
  – v(t)=v, w(t)=0 during a
    time interval ∆t , the
    robot moves in the
    direction its pointing a
    distance v ∆t.



                               91
Omidirectional




          Swedish Wheel
                          92
Car Drive (Ackerman Steering)
              • Used in motor vehicles,
                the inside front wheel is
                rotated slightly sharper
                than the outside wheel
                (reduces tire slippage).
              • Ackerman steering
                provides a fairly accurate
                dead-reckoning solution
                while supporting traction
                and ground clearance.
       R
              • Generally the method of
                choice for outdoor
                autonomous vehicles.


                                             93
           Ackerman Steering




                                                  R
where
   d = lateral wheel separation
   l = longitudinal wheel separation
   i = relative steering angle of inside wheel
   o = relative steering angle of outside wheel
   R=distance between ICC to centerline of the vehicle

                                                         94
             Ackerman Steering
• The Ackerman Steering equation:
                          d                cos
   – :
         coti  cot o            cot 
                          l                sin 



                                     cot i  cot o
                                       Rd /2 Rd /2
                                            
                                         l      l
                                       d
                                     
                                       l
                       R


                                                       95
       Ackerman Steering
Equivalent:




                           96
 Kinematic model for car-like robot
• Control Input
• Driving type: Forward wheel drive



 Y            x, y
                             { x, y ,  ,  }
                                         u1 : forward vel
                             {u1 , u2 } u : steering vel
                                          2
                              { 1 ,  2 }

                          X
                                                            97
 Kinematic model for car-like robot

x  u1 cos

                                      x, y
y  u1 sin 
                            Y
                                              
  u1 tan 
      l                               
  u2
 
 non-holonomic constraint:
                                                            X
x sin   y cos  0
                               u1
                                 u2
                                      : forward velocity
                                      : steering velocity

                                                                98
                Dynamic Model
• Dynamic model               Y                  x, y
                                                        

                                                 



                                                            X
 m 0 0     sin  
           x                   cos 0 
                                  f 
               cos    sin  0  1 
 0 m 0  y                             f 
0 0 I     0           0      1  2 
                                


                                                                99
                   Summary
• Mobot: Mobile Robot
• Classification of wheels
  –   Fixed wheel
  –   Centered orientable wheel
  –   Off-centered orientable wheel (Caster Wheel)
  –   Swedish wheel
• Mobile Robot Locomotion
  – Degrees of mobility
  – 5 types of driving (steering) methods
• Kinematics of WMR
• Basic Control
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            Course Topics
• Introduction
• Mobile Robot Locomotion
   – Classification of wheels
• Mobile Robot Kinematics
• Perception
• Mobile Robot Localization
• Planning and Navigation
• Related Papers
Thank you!




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posted:5/5/2011
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