Title:
INFORMED EXPLORATION ALGORITHMS FOR ROBOT MOTION PLANNING AND LEARNING
INFORMED EXPLORATION ALGORITHMS FOR ROBOT MOTION PLANNING AND LEARNING
| dc.contributor.advisor | Tsiotras, Panagiotis | |
| dc.contributor.author | Joshi, Sagar Suhas | |
| dc.contributor.committeeMember | Hutchinson, Seth | |
| dc.contributor.committeeMember | Gombolay, Matthew | |
| dc.contributor.committeeMember | Ravichandar, Harish | |
| dc.contributor.committeeMember | Boots, Byron | |
| dc.contributor.department | Aerospace Engineering | |
| dc.date.accessioned | 2022-05-18T19:30:08Z | |
| dc.date.available | 2022-05-18T19:30:08Z | |
| dc.date.created | 2022-05 | |
| dc.date.issued | 2022-05-03 | |
| dc.date.submitted | May 2022 | |
| dc.date.updated | 2022-05-18T19:30:08Z | |
| dc.description.abstract | Sampling-based methods have emerged as a promising technique for solving robot motion-planning problems. These algorithms avoid a priori discretization of the search-space by generating random samples and building a graph online. While the recent advances in this area endow these randomized planners with asymptotic optimality, their slow convergence rate still remains a challenge. One of the reasons for this poor performance can be traced to the widely used uniform sampling strategy that na ̈ıvely explores the entire search-space. Having access to an intelligent exploration strategy that can focus search, would alleviate one of the critical bottlenecks in speeding up these algorithms. This thesis endeavors to tackle this problem by presenting exploration algorithms that leverage different sources of information available during planning time. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/66535 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Robotics | |
| dc.subject | Motion Planning | |
| dc.subject | Learning for Robotics | |
| dc.title | INFORMED EXPLORATION ALGORITHMS FOR ROBOT MOTION PLANNING AND LEARNING | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Tsiotras, Panagiotis | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.contributor.corporatename | College of Engineering | |
| local.relation.ispartofseries | Doctor of Philosophy with a Major in Aerospace Engineering | |
| relation.isAdvisorOfPublication | bd4969ec-a869-452f-81f1-9f2dc8118b3c | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| relation.isSeriesOfPublication | f6a932db-1cde-43b5-bcab-bf573da55ed6 | |
| thesis.degree.level | Doctoral |