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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

Model Independent Visual Servoing

CMPUT 610 Literature Reading Presentation

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
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
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
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
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 servoing1
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.
  • 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
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
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
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
“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
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) ).
  • 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
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
Literature Links
  • 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.