Model Independent Visual Servoing

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# Model Independent Visual Servoing - PowerPoint PPT Presentation

Model Independent Visual Servoing. CMPUT 610 Literature Reading Presentation Zhen Deng. Introduction . Summaries and Comparisons of Traditional Visual Servoing and Model independent Visual Servoing emphasizing on the latter.

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### Model Independent Visual Servoing

Zhen Deng

Introduction
• Summaries and Comparisons of Traditional Visual Servoing and Model independent Visual Servoing emphasizing on the latter.
• Works are mostly from Jenelle A. Piepmeier’s thesis and Alexandra Hauck’s thesis
Visual Servo
• Visual servo control has the potential to provide a low-cost, low-maintenance automation solution for unstructured industries and environments.
• Robotics has thrived in ordered domains, it has found challenges in environments that are not well defined.
• Precise knowledge of the robot kinematics, the camera model, or the geometric relationship between the camera and the robot systems is assumed.
• Need to know the exact position of the end-effector and the target in the Cartesian Space.
• Require lots of calculation.
Forward Kinematics
• The Denavit-Hartenberg Notation:

i-1 T i = Rotz(q) . Transz(d) . Rotx(a) . Trans(a)

• Transformation

0 T e=0 T 11 T 22 T 3 … n-1 T n n T e

Jacobian by Differential
• Velocity variables can transformed between joint space and Euclidean space using Jacobian matrices
• Dx = J * Dq
• Dq = J \ Dx
• Jij = ¶qi/ ¶xj
Model Independent Visual Servoing
• An image-based Visual Servoing method.
• Could be further classified as dynamic look-and-move according to the classification scheme developed by Sanderson and Weiss.
• Estimate the Jacobian on-line and does not require calibrated models of either of the camera configuration or the robot kinematics.
History
• Martin Jagersand formulates the visual Servoing problem as a nonlinear least squares problem solved by a quasi-Newton method using Broyden Jacobian estimation.
• Base on Martin’s work, Jenelle P adds a frame to solve the problem of grasping a moving target.
• me ? …
Reaching a Stationary Target
• Residual error f(q) = y(q) - y*.
• Goal: minimize f(q)
• Df = fk - fk-1
• Jk = Jk-1 + (Df-Jk-1Dq) DqT/ DqTDq
• qk+1 = qk -J-1kfk
Tracking the moving object
• Interaction with a moving object, e.g. catching or hitting it, is perhaps the most difficult task for a hand-eye system.
• Most successful systems presented in paper uses precisely calibrated, stationary stereo camera systems and image-processing hardware together with a simplified visual environment.
Peter K. Allen’s Work
• Allen et al. Developed a system that could grasp a toy train moving in a plain. The train’s position is estimated from(hardware-supported) measurements of optic flow with a stationary,calibrated stereo system.
• Using a non-linear filtering and prediction, the robot tracks the train and finally grasps it.
“Ball player”
• Andersson’s ping-pong player is one of the earliest “ball playing” robot.
• Nakai et al developed a robotic volleyball player.
Jenelle’s modification to Broyden
• Residual error f(q,t) = y(q) - y*(t).
• Goal: minimize f(q,t)
• Df = fk - fk-1
• Jk = Jk-1 + (Df - Jk-1Dq + (¶ y*(t)/ ¶t *Dt) ) DqT/ DqTDq
• qk+1 = qk -(JkTJk)-1 JkT (fk - (¶ y*(t)/ ¶t *Dt) ).
Convergence
• The residual error converges as the iterations increasing.
• While the static method does not.
• The mathematics proof of this result could be found in Jenelle’s paper.
Future work ?
• Analysis between the two distinct ways of computing the Jacobian Matrix.
• Solving the tracking problem without the knowledge of target motion.
• More robust … ?
• http://mime1.gtri.gatech.edu/imb/projects/mivs/vsweb2.html
• A Dynamic Quasi-Newton Method for Uncalibrated Visual Servoing by Jenelle al
• Automated Tracking and Grasping of a Moving Object with a Robotic Hand-Eye System. By Peter K. Allen
Summary
• Model Independent approach is proved to be more robust and more efficient.