A Robust And Object-Independent Robot Visual Positioning System
| dc.contributor.author | Dhanesh Ramachandram | |
| dc.date.accessioned | 2016-10-17T07:23:25Z | |
| dc.date.available | 2016-10-17T07:23:25Z | |
| dc.date.issued | 2003-06 | |
| dc.description.abstract | This work proposes a robotic system that may learn to perform visual positioning. The task of the visual positioning system is to re-position a robot manipulator from any arbitrary initial pose to a pre-defined reference pose. A neural network is used to perform the non-linear sensory-motor transformation between the features extracted from the image and the 3D pose of the robot. The emphasis of the research is on the image representation and the positioning accuracy achievable by the proposed system and as such, the dynamic issues involved in real-time visual control are not considered. This work presents an implementation of a modular structured illumination unit for visual servoing applications. which may be mounted on the end-effector along with the camera. The laser structured illumination unit projects a grid pattern onto the target object. The projected pattern prominently reveals the local surface geometry of the object, and this method proves useful for targets that lack visual complexity to enable feature localisation using passive illumination. An added advantage of structured illumination for visual servoing is the fact that the appearance of the observed pattern is dependant on the projection angle and the surface structure of the object. If the information encoded by the projected pattern is captured efficiently, a robust visual servoing system may be constructed. Subsequently, this thesis examines various methods of representing the projected pattern. Instead of recovering the 3D surface description as in typical structured illumination approaches, global image features are used to provide a global description of the image. The use of global image features to characterise the projected patterns effectively makes the feature extraction object-independent, robust to occlusions and missing features, and does not require any form of feature labelling or correspondence matching. Several global image descriptors are examined. In the first approach, low-order image moment terms are used to provide geometrical description of the observed image. The second approach utilises the Discrete Wavelet Transform (DWT) as an effective means to extract salient features from the image projection histogram of the observed image. Selected coefficients of this transform are then used as image features for visual positioning. Finally, an approach based on the histogram of edge directions is evaluated as a global image feature for visual positioning. The translation invariant shape information encoded by the edge direction histogram is augmented using low-order geometric moments to capture the translations of the projected pattern in the image. Statistical properties of the histogram and the coefficients of the discrete wavelet transform of the edge direction histogram are then used as input features to the neural network. In this work, a recursive-positioning scheme is used to move the robot to the desired pose in a succession of motion steps. A thorough analysis of the attainable positioning accuracy of the proposed approach is made with respect to positioning complexity and two sensor-camera configurations. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2759 | |
| dc.subject | Robotic system that may learn | en_US |
| dc.subject | to perform visual positioning | en_US |
| dc.title | A Robust And Object-Independent Robot Visual Positioning System | en_US |
| dc.type | Thesis | en_US |
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