Faculty of Graduate Studies and Research, University of Regina
Predictive visual servoing; uncertainty analysis and probabilistic robust frameworks
Abstract
dc:description.abstractMotion control of robots in unstructured environments is a challenging task. The utilization of cameras as an information-rich sensor shows promise. In this context, image-based visual predictive controllers have gained attention due to their optimal-ity and constraint-handling capabilities. However, their performance deteriorates in presence of uncertainties in the robotic platforms, system models, and measurements. This work proposes a set of robust image-based visual predictive control methods that overcome the shortcomings of the previous visual servoing methods in the presence of uncertainties. In this dissertation, we have proposed a set of adaptive, stochastic, risk-averse, and learning-based visual servoing schemes that improve the performance and constraint compliance of visual servoing systems compared to their classical coun-terparts. The validity of the proposed control frameworks has been evaluated on a 6-DOF serial industrial manipulator and a model unmanned aerial vehicles via various experiments and simulations.
Degree
thesis:*- Name thesis:degree_name
- Doctor of Philosophy (PhD)
- Discipline thesis:degree_discipline
- Engineering - Industrial Systems
- Grantor dc:publisher
- Faculty of Graduate Studies and Research, University of Regina
- Year dc:date.issued
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Sajjadi, Sina
- Advisors dc:contributor.advisor
-
- Mehrandezh, Mehran
- Janabi-Sharifi, Farrokh
- Committee members dc:contributor.committeemember
-
- Dai, Liming
- Stilling, Denise
- Paranjape, Raman
- Mouhoub, Malek
Rights
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- OAI identifier oai:identifier
- oai:uregina.scholaris.ca:10294/16179