2026-09-22T08:49:18Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129898 2025-10-25 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms The student, Mahshid Mansouri, accepted the attached license on 2025-05-21 at 12:51. The student, Mahshid Mansouri, submitted this Dissertation for approval on 2025-05-21 at 13:05. This Dissertation was approved for publication on 2025-05-30 at 09:28. DSpace SAF Submission Ingestion Package generated from Vireo submission #22305 on 2025-10-20 at 20:14:43 Towards self-driving navigation control systems for a rideable ballbot Mansouri, Mahshid 2025-05-30 Hsiao-Wecksler, Elizabeth T. Driggs-Campbell, Katie R. Hsiao-Wecksler, Elizabeth T. Driggs-Campbell, Katie R. Amato ,. Nancy M. Norris ,. William R. Ramos, Joao Ballbot Dynamically-stable Robot Autonomous Navigation Path Planning Obstacle Avoidance Crowd Navigation Human-robot Interaction Mobility Device Assistive Technology. eng Ballbots, a class of dynamically balancing mobile robots that use a single spherical wheel for omnidirectional locomotion, offer agile maneuverability and a compact footprint, making them well-suited for navigation in crowded or confined indoor environments. While prior work has explored path planning for ballbots, few have addressed the challenges of navigating with real-world payload constraints, and none have tackled autonomous navigation on a rideable ballbot that includes a human in the control loop. This dissertation presents the implementation and evaluation of autonomous navigation algorithms for MiaPURE (Modular, Interactive, Adaptive, Personalized, Unique Rolling Experience), a human-ridable, hands-free, dynamically balancing ballbot platform developed by our research group at University of Illinois at Urbana-Champaign (Human Dynamics and Controls Lab). This dissertation spans three investigations. First, we integrated and benchmarked a path planning technique on MiaPURE as a heavy-payload ballbot, demonstrating its performance across static and single dynamic obstacle environments at various payloads and speeds. Second, we extended this architecture to include a reactive local planner capable of predicting and avoiding multiple moving agents using Kalman filter-based pedestrian tracking, enabling crowd-aware navigation in low to moderately dense environments. Finally, we evaluated MiaPURE in rideable scenarios with human subjects, examining how different levels of autonomy, including driver control, shared control, and full autonomy, affect user performance, cognitive load, and safety. Together, these studies contribute to the advancement of real-world ballbot navigation by addressing challenges related to underactuated dynamics, obstacle avoidance in human-centric environments, and the integration of human-robot shared control. The findings lay the groundwork for developing future assistive mobility systems that combine dynamic stability, intelligent navigation, and user-centered interaction. 2025-08 Text https://hdl.handle.net/2142/129898 Copyright 2025 Mahshid Mansouri Mechanical Sci & Engineering Mechanical Engineering University of Illinois Urbana-Champaign Ph.D. Dissertation