Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Automotive Engineering
Committee Chair/Advisor
Yunyi Jia
Committee Member
Beshah Ayalew
Committee Member
Bing Li
Committee Member
Ge Lv
Abstract
A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological signal-based detection techniques, offering insights into how robot behaviors impact human comfort. In addition, comfort-aware robot behavior control strategies were designed and validated, demonstrating the feasibility of dynamically adapting robot actions to enhance user comfort. Moreover, comparative analyses between reality-based and VR-based HRC environments were carried out to explore the feasibility of using immersive technologies for comfort evaluation and adaptation. Building upon these prior efforts, the proposed research seeks to further enhance human comfort in HRC through three main directions: (1) developing a comfort-centric task allocation framework using a Markov Decision Process (MDP) to generalize and extend comfort modeling to more complex real-world collaboration tasks; (2) integrating large language models (LLMs) for adaptive planning, utilizing an RLHF-inspired approach to align task planning strategies with human comfort preferences; and (3) enhancing robot intention communication through augmented reality (AR), ensuring that humans can intuitively perceive robot intentions and actions, thereby reducing uncertainty and improving overall interaction quality.
Recommended Citation
Yan, Yuchen, "Human Comfort Modeling, Measurement, and Improvement in Human–Robot Collaboration" (2025). All Dissertations. 4061.
https://open.clemson.edu/all_dissertations/4061
Author ORCID Identifier
0000-0002-9340-7904
Included in
Controls and Control Theory Commons, Ergonomics Commons, Industrial Engineering Commons, Manufacturing Commons, Robotics Commons, Signal Processing Commons