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Model Independent Visual ServoingPowerPoint Presentation

Model Independent Visual Servoing

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## PowerPoint Slideshow about ' Model Independent Visual Servoing' - lynn-nelson

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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.

Traditional Visual Servoing

- 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 … ?

Literature Links

- 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.

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