robotics chapter 6 – visual servoing
DESCRIPTION
Robotics Chapter 6 – Visual Servoing. Dr. Amit Goradia. Topics. Introduction – 2 hrs Coordinate transformations – 6 hrs Forward Kinematics - 6 hrs Inverse Kinematics -6 hrs Velocity Kinematics - 2 hrs Trajectory Planning - 6 hrs Robot Dynamics (Introduction) - 2 hrs - PowerPoint PPT PresentationTRANSCRIPT
RoboticsChapter 6 – Visual Servoing
Dr. Amit Goradia
Topics
• Introduction – 2 hrs• Coordinate transformations – 6 hrs• Forward Kinematics - 6 hrs• Inverse Kinematics - 6 hrs• Velocity Kinematics - 2 hrs• Trajectory Planning - 6 hrs• Robot Dynamics (Introduction) - 2 hrs• Force Control (Introduction) - 1 hrs• Task Planning - 6 hrs• Machine Vision - 6 hrs
Visual Servoing
• Control the movement of a robot using information collected from cameras.
• Operates in closed loop. • Provides better accuracy than look and
move systems
Camera Configurations
End-Effector Mounted Fixed
Servoing Architectures
• Position based.
• Coordinates extracted from the image
• Control law derived from extracted coordinates
Servoing Architectures
• Image based
• Positions are not extracted from image
• Control directly applied in image space
Position Based
• 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
target
End-effector
Image Based
• 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
Image of target
Image of end effector
EOL and ECL Configurations
• 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
Position Based Algorithm
• Position based– Estimation of relative pose– Computation of error between current pose
and target pose– Movement of robot
p1
p2
Position Based Point Alignment
• Goal: Bring e to 0 by moving p1
– 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
e = |p2m – p1m|u = k*(p2m – p1m)
Image based Point Alignment
• 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
Goal: Bring e to 0 by moving p1
e = |u1m – v1m| + |u2m – v2m|
Image Jacobian
• f is the feature point in image space
• r is the real-world position
• Image Jacobian relates the rates of change from f to r
• Image Jacobian for perspective projection model
rrJf )(