Author ORCID Identifier
Semester
Fall
Date of Graduation
2024
Document Type
Dissertation
Degree Type
PhD
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
Yu Gu
Committee Member
Jason Gross
Committee Member
Guilherme Pereira
Committee Member
Gianfranco Doretto
Committee Member
Xi Yu
Abstract
From space and deep-sea exploration to disaster response and environmental monitoring, autonomous robots are essential for advancing science, improving safety, and addressing critical challenges. This dissertation introduces a novel open-source strategy for autonomous robotic exploration: the Semantically-Guided Exploration (SGE) framework. Designed for ground vehicles, SGE integrates semantic understanding into the autonomous exploration process, improving decision-making in complex environments. Specifically, the proposed sampling-based approach uses the information from the semantic segmentation of RGB images and depth images to guide the robot's selection of exploration goals. This method enables the robot to steer away from potential dangers such as large rocks and water, while prioritizing a specific type of terrain or objective, such as staying on trails, making exploration safer and more flexible. Additionally, an exploration manager framework is proposed to process these waypoints. It optimizes viewpoint selection via a Traveling Salesman Problem (TSP) formulation in a receding-horizon manner, ensuring robots make intelligent, context-driven decisions while navigating uncertain terrains. Furthermore, a new learning-based direct sampling method is presented, which aims to mimic human-like exploration behaviors, by teaching robots to select waypoints from human-provided examples directly from raw data inputs. The methods are extensively evaluated in both in simulation and in real-world settings, including the university campus, indoor corridors, and underground mining environments. Experimental results validate the framework's effectiveness, demonstrating that SGE provides significant advantages increasing flexibility and safety, while maintaining competitive performance with state-of-the-art exploration techniques in benchmarked tests evaluating volumetric data and exploration distance. Overall, the SGE framework enhanced environmental understanding and decision-making capabilities, enabling autonomous robots to operate effectively in less constrained and more challenging environments.
Recommended Citation
Arend Tatsch, Christopher Alexander, "Enhancing Robotic Exploration through Semantically-Guided Sampling Strategies" (2024). Graduate Theses, Dissertations, and Problem Reports. 12637.
https://researchrepository.wvu.edu/etd/12637
Included in
Electrical and Computer Engineering Commons, Mechanical Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Robotics Commons
