Theses and Dissertations
ORCID
https://orcid.org/0009-0006-6927-436X
Advisor
Lee, Seunghan
Committee Member
Strawderman, Lesley
Committee Member
Smith, Brian
Date of Degree
5-16-2025
Original embargo terms
Embargo 2 years
Document Type
Graduate Thesis - Open Access
Major
Engineering (Industrial Systems)
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
Autonomous mobile robots (AMRs) serve diverse purposes across various settings to assist with material handling operations. AMRs rely on advanced control methods to manage and execute tasks such as motion planning, environmental perception, navigation, and obstacle avoidance. As warehouse environments become increasingly complex, there is a growing demand for AMR sensing technology to maintain maximum safety. This thesis proposes to leverage optical flow utilizing AMR vision data to incorporate real-time sensory information into the control strategy framed as Markov Decision Processes (MDP). The proposed model investigates how dynamic and real-time sensing information can be incorporated into MDP models via optical flow, capturing AMR’s surroundings to enhance situational awareness and, consequently, pedestrian safety. Moreover, to maximize the model’s validity and practical application, it was tested within a physics-based simulation. Experimental results validate that the optical flow-based results outperform the standard MDP model regarding AMR efficiency, proving its enhanced effectiveness and reliability.
Recommended Citation
Gibson-Todd, Seth Morgan, "Optical flow-based Markov decision process control strategies for autonomous mobile robots in dynamic environments" (2025). Theses and Dissertations. 6489.
https://scholarsjunction.msstate.edu/td/6489
