Efficient nearest-neighbor search algorithms for sub-Riemannian geometries

The Motion Planning problem has been at the core of a significant amount of research in the past decades and it has recently gained traction outside academia with the rise of commercial interest in self-driving cars and autonomous aerial vehicles. Among the leading algorithms to tackle the problem are sampling-based planners, such as Probabilistic Road Maps (PRMs), Rapidly-exploring Random Trees (RRTs) and a large number of variants thereof. In this thesis, we focus on a crucial building block shared by these algorithms: nearest-neighbor search. While nearest-neighbor search is known as the asymptotically dominant bottleneck of sampling-based planners, popular algorithms to efficiently identify neighbors are limited to robots capable of unconstrained motions, commonly referred to as holonomic.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Varricchio, Valerio.

Advisor dc:contributor.advisor

• Emilio Frazzoll.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122500

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122500