| Author | Supattra Plermkamon |
| Call Number | AIT Diss no.ISE-02-01 |
| Subject(s) | Robots--Control systems
Neural networks (Computer science)
|
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering, School of Advanced Technologies |
| Publisher | Asian Institute of Technology |
| Series Statement | Dissertation ; no. ISE-02-01 |
| Abstract | Versatile v1s1on systems are being employed increasingly for currently automated visual
feedback intelligent robotic control to perform complex manufacturing tasks especially in
tracking and grasping dynamic object on the conveyor. They can also make work-cell to use
less structured feeding techniques. The vision systems are used for interpreting capabilities
without previous knowledge of the arbitrary object information and they can automatically
generate the optimum tracking trajectory for the robot. However the numerous number of
newly part products which are manufactured cause the troublesome work to manufacturing
engineers and the need to pre-program to make severe demand for the precision of operation
especially in repetitive tasks will be increased, as well as, the currently automated visual
feedback robot systems use vision and robot system as separate tools. The best solution for
these kind of problems still can not be implemented successfully with ease. Therefore, this
study tried to develop the new single adaptive prediction and execution algorithm for the
picking and placing static and dynamic object in the real-time operation robot work-cell. The
operator can feel convenient to operate all tasks only by this single algorithm. The proposed
system was designed by integrated a stationary monocular CCD camera with off-the-shelf
frame grabber and an industrial robot operation into a single application on MATLAB. The
new adaptive linear robot control system for a robot work-cell that can visually track and
intercept static and dynamic objects undergoing arbitrary motion anywhere along its predicted
trajectory within the robot's workspace, is presented in this study. The proposed system used
a combination of the model based object recognition technique and L VQ network for
classifying static objects which without overlapping. The proposed robot control system also
used optical flow technique to determine the target trajectory and used the MADALINE
network to generate a predicted robot trajectory based on visual servoing in both off-line and
on-line processes. On-line planning program can operate without the need to pre-program in
excruciating detail all the required tasks and any change in a task is possible without changing
the robot program. Necessity of determining model of the robot, camera for all the static and
dynamic objects and environment will be eliminated. This proposed system can operate
efficiently on both static and dynamic object tracking. The conveyor speeds that give the
smallest error value are 35-86 millimeter/second with robot fully at speed of two
meter/second. In case of static object tracking, the system can classify the arbitrary object that
we want to select accurately but in case of dynamic object tracking, the proposed system can
not classify and the implementation is done only for one dimensional direction because of the
time constraint. After learning process on robot, it is shown that KUKA robot is capable
adaptability of tracking and intercepting both static and dynamic objects at an optimal
rendezvous point on the conveyor accurately in real time. |
| Year | 2002 |
| Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. ISE-02-01 |
| Type | Dissertation |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Afzulpurkar, Nitin V.; |
| Examination Committee(s) | Bohez, Ir. Erik L.J.;
Pham Minh Dung;Lee, Gerald Seet Gim ; |
| Scholarship Donor(s) | Ministry of University Affairs; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2002 |
