Date of Award
Bachelor of Science
Dr. Jory Denny
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.
Qin, David, "Biasing Medial Axis Rapidly-Exploring Random Trees with Safe Hyperspheres" (2020). Honors Theses. 1521.