abstract / Mechanical Engineering / Master / Master of Philosophy

Significant efforts have been expended by the railroad industry to make operations safer and more efficient through the intelligent use of sensor data. This work proposes to take the technology one step further to use this data for the control of physical systems designed to automate hazardous railroad operations, particularly those that require humans to interact with moving trains. To accomplish this, application specific requirements must be established to design self-contained machine vision and robotic solutions to eliminate the risks associated with existing manual operations. Present-day rail yard operations have been identified as good candidates to begin development. Manual uncoupling, in particular, of rolling stock in classification yards has been investigated. To automate this process, an intelligent robotic system must be able to detect, track, approach, contact, and manipulate constrained objects on equipment in motion. This work presents multiple prototypes capable of autonomously uncoupling full-scale freight cars using feedback from its surrounding environment. Geometric image processing algorithms and machine learning techniques were implemented to accurately identify cylindrical objects in point clouds generated in real-vi time. Unique methods fusing velocity and vision data were developed to synchronize a pair of moving rigid bodies in real-time. Multiple custom end-effectors with in-built compliance and fault tolerance were designed, fabricated, and tested for grasping and manipulating cylindrical objects. Finally, an event-driven robotic control application was developed to safely and reliably uncouple freight cars using data from 3D cameras, velocity sensors, force/torque transducers, and intelligent end-effector tooling. Experimental results in a lab setting confirm that modern robotic and sensing hardware can be used to reliably separate pairs of rolling stock up to two miles per hour. Additionally, subcomponents of the autonomous pin-pulling system (APPS) were designed to be modular to the point where they could be used to automate other hazardous, labor-intensive tasks found in U.S. classification yards. Overall, this work supports the deployment of autonomous robotic systems in semi-unstructured yard environments to increase the safety and efficiency of rail operations. / text

Thesis (M.E.) -- University of Adelaide, Dept. of Civil Engineering, 1978.

Thesis (Ph.D.)--University of Wollongong, 2003. / Includes appendices. Typescript. Bibliographical references: leaf 216-227.

Thesis (Ph.D.)--University of Delaware, 2008. / Principal faculty advisor: Susan Strasser, Dept. of History. Includes bibliographical references.

Thesis--University of Missouri, School of Mines and Metallurgy, 1876. / T. H. Millsaps determined to be Thomas H. Millsaps from "1874-1999 MSM-UMR Alumni Directory". The entire thesis text is included in file. Holograph [Handwritten and illustrated in entirety by author]. Title from title screen of thesis/dissertation PDF file (viewed November 21, 2008)

Thesis--University of Missouri, School of Mines and Metallurgy, 1876. / T. H. Millsaps determined to be Thomas H. Millsaps from "1874-1999 MSM-UMR Alumni Directory". The entire thesis text is included in file. Holograph [Handwritten and illustrated in entirety by author]. Title from title screen of thesis/dissertation PDF file (viewed November 21, 2008)

Thesis (M.S.)--Oregon Graduate Institute of Science and Technology, 1983.

Thesis (doctoral)--Friedrich-Alexander-Universität zu Erlangen.

Thesis (B.S.)--University of Missouri, School of Mines and Metallurgy, 1895. / The entire thesis text is included in file. Holograph [Handwritten and illustrated in entirety by author]. J. E. Kirkham determined to be John Edward Kirkham from "Forty-First Annual Catalogue. School of Mines and Metallurgy, University of Missouri". Title from title screen of thesis/dissertation PDF file (viewed January 30, 2009)