Predictive visual servoing; uncertainty analysis and probabilistic robust frameworks
| dc.contributor.advisor | Mehrandezh, Mehran | |
| dc.contributor.advisor | Janabi-Sharifi, Farrokh | |
| dc.contributor.author | Sajjadi, Sina | |
| dc.contributor.committeemember | Dai, Liming | |
| dc.contributor.committeemember | Stilling, Denise | |
| dc.contributor.committeemember | Paranjape, Raman | |
| dc.contributor.committeemember | Mouhoub, Malek | |
| dc.contributor.externalexaminer | Xie, Wen-Fang | |
| dc.date.accessioned | 2023-12-18T17:06:15Z | |
| dc.date.available | 2023-12-18T17:06:15Z | |
| dc.date.issued | 2023-06 | |
| dc.description | A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Industrial Systems Engineering, University of Regina. xx, 214 p. | |
| dc.description.abstract | Motion 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. | |
| dc.description.authorstatus | Student | en |
| dc.description.peerreview | yes | en |
| dc.identifier.tcnumber | TC-SRU-16179 | |
| dc.identifier.thesisurl | https://ourspace.uregina.ca/bitstreams/8e30f601-4824-4a2f-83fd-671f3e613094/download | |
| dc.identifier.uri | https://hdl.handle.net/10294/16179 | |
| dc.language.iso | en | en |
| dc.publisher | Faculty of Graduate Studies and Research, University of Regina | en |
| dc.title | Predictive visual servoing; uncertainty analysis and probabilistic robust frameworks | |
| dc.type | Thesis | en |
| thesis.degree.department | Faculty of Engineering and Applied Science | |
| thesis.degree.discipline | Engineering - Industrial Systems | |
| thesis.degree.grantor | University of Regina | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy (PHD) | en |