Vision-Based Motion Control of Robots Azad Shademan Guest Lecturer CMPUT 412 – Experimental...
-
date post
20-Jan-2016 -
Category
Documents
-
view
215 -
download
0
Transcript of Vision-Based Motion Control of Robots Azad Shademan Guest Lecturer CMPUT 412 – Experimental...
Vision-Based Motion Control of Vision-Based Motion Control of RobotsRobots
Azad ShademanGuest Lecturer
CMPUT 412 – Experimental RoboticsComputing Science, University of Alberta
Edmonton, Alberta, CANADA
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Vision-Based Control
current
desired
Left Image Right Image
A
A
A
B
BB
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Vision-Based Control
Left Image Right Image
B
BB
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Vision-Based Control Feedback from visual sensor (camera) to
control a robot Also called “Visual Servoing” Is it any difficult?
Images are 2D, the robot workspace is 3D 2D data 3D geometry
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Where is the camera located? Eye-to-Hand
e.g.,hand/eye coordination
Eye-in-Hand
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Visual Servo Control law Position-Based:
Robust and real-time pose estimation + robot’s world-space (Cartesian) controller
Image-Based:Desired image features seen from cameraControl law entirely based on image features
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Position-Based
Desired pose
Estimated pose
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Image-Based
Desired Image feature
Extracted image feature
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Visual-motor Equationx1
x2
x3 x4
q=[q1 … q6]
Visual-Motor Equation
This Jacobian is important for motion control.
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Visual-motor JacobianImage spacevelocity
Joint spacevelocity
A
A
B
B
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Image-Based Control Law
1. Measure the error in image space
2. Calculate/Estimate the inverse Jacobian
3. Update new joint values
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Image-Based Control Law
Desired Image feature
Extracted image feature
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Jacobian calculation Analytic form available if model is known.
Known model Calibrated
Must be estimated if model is not known
Unknown model Uncalibrated
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Image Jacobian (calibrated) Analytic form depends on depth estimates.
Camera/Robot transform required. No flexibility.
CameraVelocity
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Image Jacobian (uncalibrated) A popular local estimator:
Recursive secant method (Broyden update):
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Relaxed model assumptions
Traditionally: Local methods No global planning Difficult to show
asymptotic stability condition is ensured
The problem of traditional methods is the locality.
Calibrated vs. Uncalibrated
Model derived analytically Global asymptotic
stability Optimal planning is
possible A lot of prior knowledge
on the model
Global Model Estimation (Research result)
Optimal trajectory planning
Global stability guarantee
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Synopsis of Global Visual Servoing Model Estimation (Uncalibrated) Visual-Motor Kinematics Model Global Model
Extending Linear Estimation (Visual-Motor Jacobian) to Nonlinear Estimation
Our contributions:K-NN Regression-Based EstimationLocally Least Squares Estimation
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Local vs. Global
Key idea: using only the previous estimation to estimate the Jacobian
1. RLS with forgetting factor Hosoda and Asada ’94
2. 1st Rank Broyden update: Jägersand et al. ’97
3. Exploratory motion: Sutanto et al. ‘98
4. Quasi-Newton Jacobian estimation of moving object: Piepmeier et al. ‘04
Key idea: using all of the interaction history to estimate the Jacobian
Globally-Stable controller design
Optimal path planning Local methods don’t!
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
3 NN
K-NN Regression-based Method
q1
q2
x1
q1
q2
?
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
q1
q2
x1 ?
KNN(q)
(X,q)
Locally Least Squares Method
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Experimental Setup
Puma 560 Eye-to-hand configuration Stereo vision Features: projection of the end-effector’s
position on image planes (4-dim) 3 DOF for control
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Measuring the Estimation Error
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Global Estimation Error
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Noise on Estimation Quality
KNN
LLS
With increasing noise level, the error decreases
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Effect of Number of Neighbors
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Conclusions Presented two global methods to learn the visual-
motor function LLS (global) works better than the KNN (global) and
local updates. KNN suffers from the bias in local estimations Noise helps system identification
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Eye-in-Hand Simulator
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Eye-in-Hand Simulator
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Eye-in-Hand Simulator
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Eye-in-Hand Simulator
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Mean-Squared-Error
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Task Errors
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Questions?
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Position-Based Robust and real-time relative pose
estimation Extended Kalman Filter to solve the
nonlinear relative pose equations. Cons:
EKF is not the optimal estimator.Performance and the convergence of pose
estimates are highly sensitive to EKF parameters.
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Overview of PBVS
2D-3D nonlinear point correspondences
IEKFWhat kind of nonlinearity?
T. Lefebvre et al. “Kalman Filters for Nonlinear Systems: A Comparison of Performance,” Intl. J. of Control, vol.
77, no. 7, pp. 639-653, May 2004.
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
EKF Pose Estimation
Measurement noise
State variable
Process noise
yaw pitch roll
Measurement equation is nonlinear and must be linearized.
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Visual-Servoing Based on the Estimated Global Model
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
Control Based on Local Models
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008
In practice: Broyden 1st-rank estimation, RLS with forgetting factor, etc.
Estimation for Local Methods
A. Shademan. CMPUT 412, Vision-based motion control of robots March 4, 2008