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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

• 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 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.

• 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

• Velocity variables can transformed between joint space and Euclidean space using Jacobian matrices

• Dx = J * Dq

• Dq = J \ Dx

• Jij = ¶qi/ ¶xj

• 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.

• 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 ? …

• 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

• 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.

• 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.

• Andersson’s ping-pong player is one of the earliest “ball playing” robot.

• Nakai et al developed a robotic volleyball player.

• 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) ).

• 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.

• 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

• Model Independent approach is proved to be more robust and more efficient.