Title:
Multistrategy Learning in Reactive Control Systems for Autonomous Robotic Navigation
Multistrategy Learning in Reactive Control Systems for Autonomous Robotic Navigation
| dc.contributor.author | Ram, Ashwin | |
| dc.contributor.author | Santamaria, Juan Carlos | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.date.accessioned | 2008-06-09T14:54:31Z | |
| dc.date.available | 2008-06-09T14:54:31Z | |
| dc.date.issued | 1993 | |
| dc.description.abstract | This paper presents a self-improving reactive control system for autonomous robotic navigation. The navigation module uses a schema-based reactive control system to perform the navigation task. The learning module combines case-based reasoning and reinforcement learning to continuously tune the navigation system through experience. The case-based reasoning component perceives and characterizes the system's environment, retrieves an appropriate case, and uses the recommendations of the case to tune the parameters of the reactive control system. The reinforcement learning component refines the content of the cases based on the current experience. Together, the learning components perform on-line adaptation, resulting in improved performance as the reactive control system tunes itself to the environment, as well as on-line case learning, resulting in an improved library of cases that capture environmental regularities necessary to perform on-line adaptation. The system is extensively evaluated through simulation studies using several performance metrics and system configurations. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/22437 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Adaptive control | en_US |
| dc.subject | Case-based reasoning | en_US |
| dc.subject | Reactive control | en_US |
| dc.subject | Reinforcement learning | en_US |
| dc.subject | Robot navigation | en_US |
| dc.title | Multistrategy Learning in Reactive Control Systems for Autonomous Robotic Navigation | en_US |
| dc.type | Text | |
| dc.type.genre | Paper | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | Mobile Robot Laboratory | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
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