| dc.contributor.advisor | Nicholas Roy. | en_US |
| dc.contributor.author | Wei, Yuan | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2010-03-24T20:36:11Z | |
| dc.date.available | 2010-03-24T20:36:11Z | |
| dc.date.copyright | 2009 | en_US |
| dc.date.issued | 2009 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/52773 | |
| dc.description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. | en_US |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Includes bibliographical references (p. 57-59). | en_US |
| dc.description.abstract | Rapidly-Exploring Random Trees (RRT) have been successfully applied to many different robotics systems for motion and manipulation planning under non-holonomic constraints. However, the conventional RRT algorithm may perform poorly in the presence of noise and uncertainty. This thesis proposes a modified form of the algorithm that seeks to reduce the robot's uncertainty in its estimate of the target by choosing solutions that maximize the opportunities for the robot's sensors to perceive the target. This new perception-guided technique will be tested in simulation and compared to the conventional RRT as well as other approaches taken from the literature. The ultimate goal is to integrate this method with a semi-autonomous robotic forklift charged with the task of approaching and picking up a loaded wooden pallet over rough terrain. | en_US |
| dc.description.statementofresponsibility | by Yuan Wei. | en_US |
| dc.format.extent | 59 p. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | A perception-guided approach to motion and manipulation planning | en_US |
| dc.title.alternative | Perception-guided approach to sampling-based motion and manipulation planning under uncertainty | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M.Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 518077550 | en_US |
