Sampling and Searching Methods for Practical Motion Planning Algorithms

In its original conception, the motion planning problem considered the search of a robot path from an initial to a goal configuration. The study of motion planning has advanced significantly in recent years, in large part due to the development of highly successful sampling and searching techniques. Recent advances have influenced sampling-based motion planning algorithms to be used in disparate areas such as humanoid robotics, automotive manufacturing, spacecraft navigation, architecture, computational geography, computer graphics, and computational biology. Many of these methods work well on a large set of problems, however, they have weaknesses and limitations. This thesis expands the basic motion planning techniques to include critical concerns that are not covered by the motion planning algorithms that are in widespread use now. The technical approach is organized around three main thrusts: (1) the development of efficient nearest neighbor searching techniques for spaces arising in motion planning; (2) the development of uniform sampling techniques on these spaces to allow resolution completeness in sampling-based planning algorithms; and (3) the development of guided sampling techniques for efficient exploration on such spaces. Addressing these core issues in motion planning does not only lead to a more fundamental understanding of the problem, but also to more efficient practical algorithms.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Yershova, Ganna

Contributors dc:contributor

• LaValle, Steven M.

• Engineering, Robotics

Language dc:language

eng

Identifier

(MiAaPQ)AAI3337972

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81838