2026-09-19T13:30:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/527732022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nicholas Roy.
Wei, Yuan
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-24T20:36:11Z
2010-03-24T20:36:11Z
2009
2009
http://hdl.handle.net/1721.1/52773
518077550
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 57-59).
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.
by Yuan Wei.
M.Eng.
59 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A perception-guided approach to motion and manipulation planning
Perception-guided approach to sampling-based motion and manipulation planning under uncertainty
Thesis
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<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.</Abstract>
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