2026-09-21T03:24:59Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/122017 2024-03-01 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Norris, William R Hsiao-Wecksler, Elizabeth T Norris, William R Hsiao-Wecksler, Elizabeth T Ramos, João Sreenivas, Ramavarapu S Driggs-Campbell, Katie Wang, YuXiong 2023-12 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms The student, Yu Chen, accepted the attached license on 2023-11-27 at 11:02. The student, Yu Chen, submitted this Dissertation for approval on 2023-11-27 at 11:20. This Dissertation was approved for publication on 2023-11-30 at 17:17. DSpace SAF Submission Ingestion Package generated from Vireo submission #20016 on 2024-03-01 at 13:15:03 Deep Learning For Visual Perception Rgb-d Perception Vision-based Navigation 2023-11-30 Enabling mobile robot perception and shared-control with vision-based sensor fusion systems Chen, Yu Self-balancing Robot Robot Perception Vision Sensor Sensor Fusion Shared-control Mobile robots with various locomotion mechanisms have been developed to adapt to different environmental conditions and mission requirements. The advancement in mobile robot development has raised the requirements for robot perception to ensure the safe and smooth execution of robot missions. In the past decade, the emergence of a novel self-balancing mobile robot system, known as the ballbot, has opened up new avenues of research in the field of mobile robots. While offering enhanced maneuverability, the under-actuated dynamics of the ballbot invalidate most existing ground robot perception solutions. Moreover, when the ballbot is designed as a riding mobility device, it imposes additional demands and constraints on robot perception and human-robot shared motion control. Specifically, the ballbot’s omnidirectional capability requires operators to possess significant proficiency and experience in riding and controlling the platform effectively. To address these challenges, there is a critical need for advanced and innovative robot perception techniques and higher-level control systems that can reduce the operator’s cognitive load and ensure system safety by preventing unwanted collisions. This dissertation aims to investigate robot perception and safeguarding challenges by leveraging vision-based sensor fusion techniques and commonly available commercial sensors. The research specifically focuses on developing robot perception and shared-control systems that can be deployed on a physical ballbot prototype, adhering to strict Size-Weight-and-Power Constraints (SWaP-C), while also being generalizable to mobile robots with simpler or similar dynamic characteristics as ballbots. Text eng https://hdl.handle.net/2142/122017 Copyright 2023 Yu Chen Ph.D. Dissertation Mechanical Engineering University of Illinois at Urbana-Champaign Mechanical Sci & Engineering