Date of Award

2025

Document Type

Thesis (Master's)

Department or Program

Computer Science

First Advisor

Devin J. Balkcom

Second Advisor

Alberto Quattrini Li

Third Advisor

Muhao Chen

Abstract

This thesis presents a hierarchical motion planning framework for SoftRafts, a modular and deformable aquatic robot capable of performing locomotion and manipulation tasks on water surfaces. SoftRafts consist of soft and rigid components that enable structural reconfiguration, offering adaptability in unstructured aquatic environments.

To address the complexity of planning in high-dimensional, deformable systems, the proposed method uses a bounding-shape abstraction, specifically, enclosing circles and rectangular bounding boxes to simplify motion planning. These enclosures abstract the robot's overall shape, reducing the high-dimensional planning problem into a lower-dimensional problem. A global planner uses a probabilistic roadmap (PRM) to compute a collision-free path for the enclosing shape through environments with obstacles. At vertices along this path a local planner evaluates whether the current configuration can pass through the constrained space. If not, a deformation planner adjusts the robot's shape to minimize the enclosure size while preserving locomotion capability. This process is repeated iteratively, with real-time coordination between global navigation goals and local deformation requirements.

Available for download on Thursday, May 14, 2026

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