ssip 2004 graz© inst. for computer graphics and vision, 2004 vision guided robotics and...
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SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Guided Robotics
and Applications in Industry and Medicine
Matthias Rüther
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Contents
Robotics in General
Industrial Robotics
Medical Robotics
What can Computer Vision do for Robotics?
Vision Sensors
Issues / Problems
Visual Servoing
Application Examples
Summary
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Robotics
What is a robot?"A reprogrammable, multifunctional manipulator designed to move
material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks"
Robot Institute of America, 1979
Industrial– Mostly automatic manipulation of rigid parts with well-known shape in a
specially prepared environment.
Medical– Mostly semi-automatic manipulation of deformable objects in a
naturally created, space limited environment.
Field Robotics– Autonomous control and navigation of a mobile vehicle in an arbitrary
environment.
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Robot vs Human
Robot Advantages:
– Strength
– Accuracy
– Speed
– Does not tire
– Does repetitive tasks
– Can Measure
Human advantages:
– Intelligence
– Flexibility
– Adaptability
– Skill
– Can Learn
– Can Estimate
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Requirements:
– Accuracy– Tool Quality– Robustness– Strength– Speed – Price Production Cost– Maintenance
Industrial Robot
Production Quality
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Medical (Surgical) Robot
Requirements
– Safety– Accuracy– Reliability– Tool Quality– Price– Maintenance– Man-Machine Interface
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
What can Computer Vision do for Robotics?
Accurate Robot-Object Positioning
Keeping Relative Position under Movement
Visualization / Teaching / Telerobotics
Performing measurements
Object Recognition
Registration
Visual Servoing
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Single Perspective Camera
Multiple Perspective Cameras (e.g. Stereo Camera Pair)
Laser Scanner
Omnidirectional Camera
Structured Light Sensor
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Single Perspective Camera
XPx x43
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Multiple Perspective Cameras (e.g. Stereo Camera Pair)
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Multiple Perspective Cameras (e.g. Stereo Camera Pair)
0Fxx'T Fxl'
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Laser Scanner
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Laser Scanner
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Omnidirectional Camera
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Omnidirectional Camera
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Vision Sensors
Structured Light Sensor
Figures from PRIP, TU Vienna
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Issues/Problems of Vision Guided Robotics
Measurement Frequency
Measurement Uncertainty
Occlusion, Camera Positioning
Sensor dimensions
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Vision System operates in a closed control loop.
Better Accuracy than „Look and Move“ systems
Figures from S.Hutchinson: A Tutorial on Visual Servo Control
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Example: Maintaining relative Object Position
Figures from P. Wunsch and G. Hirzinger. Real-Time Visual Tracking of 3-D Objects with Dynamic Handling of Occlusion
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Camera Configurations:
End-Effector Mounted Fixed
Figures from S.Hutchinson: A Tutorial on Visual Servo Control
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Servoing Architectures
Figures from S.Hutchinson: A Tutorial on Visual Servo Control
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Position-based and Image Based control
– Position based: • Alignment in target coordinate system• The 3D structure of the target is rconstructed• The end-effector is tracked• Sensitive to calibration errors• Sensitive to reconstruction errors
– Image based:• Alignment in image coordinates• No explicit reconstruction necessary• Insensitive to calibration errors• Only special problems solvable• Depends on initial pose• Depends on selected features
target
End-effector
Image of target
Image of end effector
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
EOL and ECL control
– EOL: endpoint open-loop; only the target is observed by the camera
– ECL: endpoint closed-loop; target as well as end-effector are observed by the camera
EOL ECL
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Position Based Algorithm:1. Estimation of relative pose
2. Computation of error between current pose and target pose
3. Movement of robot
Example: point alignment
p1
p2
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Position based point alignment
Goal: bring e to 0 by moving p1
e = |p2m – p1m|
u = k*(p2m – p1m)
pxm is subject to the following measurement errors: sensor position, sensor calibration, sensor measurement error
pxm is independent of the following errors: end effector position, target position
p1m p2m
d
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing Image based point alignment
Goal: bring e to 0 by moving p1
e = |u1m – v1m| + |u2m – v2m|
uxm, vxm is subject only to sensor measurement error
uxm, vxm is independent of the following measurement errors: sensor position, end effector position, sensor calibration, target position
p1 p2
c1 c2
u1
u2
v1 v2
d1d2
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Example Laparoscopy
Figures from A.Krupa: Autonomous 3-D Positioning of Surgical Instruments in Robotized Laparoscopic Surgery Using Visual Servoing
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Visual Servoing
Example Laparoscopy
Figures from A.Krupa: Autonomous 3-D Positioning of Surgical Instruments in Robotized Laparoscopic Surgery Using Visual Servoing
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Registration
Registration of CAD models to scene features:
Figures from P.Wunsch: Registration of CAD-Models to Images by Iterative Inverse Perspective Matching
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Registration
Registration of CAD models to scene features:
Figures from P.Wunsch: Registration of CAD-Models to Images by Iterative Inverse Perspective Matching
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Tracking
Instrument tracking in laparoscopy
Figures from Wei: A Real-time Visual Servoing System for Laparoscopic Surgery
SSIP 2004 Graz © Inst. For Computer Graphics and Vision, 2004
Summary
Computer Vision provides accurate and versatile measurements for robotic manipulators
With current general purpose hardware, depth and pose measurements can be performed in real time
In industrial robotics, vision systems are deployed in a fully automated way.
In medicine, computer vision can make more intelligent „surgical assistants“ possible.