Author

David Qin

Date of Award

2020

Document Type

Thesis

Degree Name

Bachelor of Science

Department

Mathematics

First Advisor

Dr. Jory Denny

Abstract

Motion planning is a challenging and widely researched problem in robotics. Motion planning algorithms aim to not only nd unobstructed paths, but also to construct paths with certain qualities, such as maximally avoiding obstacles to improve path safety. One such solution is a Rapidly-Exploring Random Tree (RRT) variant called Medial Axis RRT that generates the safest possible paths, but does so slowly. This paper introduces a RRT variant called Medial Axis Ball RRT (MABallRRT) that uses the concept of clearance -- a robot's distance from its nearest obstacle -- to efficiently construct a roadmap with safe paths. The safety of the paths generated by MABallRRT and the efficiency of the procedure in solving example queries were experimentally analyzed and compared to the original RRT and Medial Axis RRT algorithms, demonstrating MABallRRT's potential effectiveness as a motion planner.

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