{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120097"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120097","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning and decision-making in spatiotemporally varying domains","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Whitman, Joshua Earl"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Chowdhary, Girish","Mehta, Prashant","Sreenivas, R.S.","Gazzola, Mattia"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:56Z","subjects":["Machine Learning","Dynamics","Gaussian Process","Computational Flow Dynamics","Traveling Salesman","Machine Vision","Koopman Operator","Agricultural Robotics","Decision And Control"],"languages":["en","eng"],"rights":["Copyright 2022 by Joshua Earl Whitman. All rights reserved."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120097","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish","Mehta, Prashant","Sreenivas, R.S.","Gazzola, Mattia"]},{"key":"dc:creator","label":"Author","values":["Whitman, Joshua Earl"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-26"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Dynamics","Gaussian Process","Computational Flow Dynamics","Traveling Salesman","Machine Vision","Koopman Operator","Agricultural Robotics","Decision And Control"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 by Joshua Earl Whitman. All rights reserved."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120097"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Joshua Whitman, accepted the attached license on 2023-04-25 at 01:18.","The student, Joshua Whitman, submitted this Dissertation for approval on 2023-04-25 at 01:31.","This Dissertation was approved for publication on 2023-04-26 at 14:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19154 on 2023-09-01 at 16:55:16","Modeling and monitoring of large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution using sensors that can only partially observe the system is a fundamental problem in many applications. In particular, in order to make intelligent decisions in robot coordination and actuation, the robot(s) need to be able to predict the full state of their evolving environment at any given time, given only sparse measurements. The purpose of this dissertation is to take steps towards addressing this challenging problem. The rapid advances in the computational power of compact systems and robotics as a whole has led to an explosion of real-world applications for such distributed cyber-physical systems. This work will present advances in this domain, both in theory and in experiments. It will begin with a description of the Kernel Observers (KO) approach and the derivation of the Evolving Gaussian Processes (E-GP) model for generalizing across similar spatiotemporally evolving systems. It will be shown that method is effective for modeling nonlinear fluid flows, and that there are indeed deep theoretical connections with Koopman operator theory and Dynamic Mode Decomposition (DMD) which have gained recognition in the Computational Flow Dynamics (CFD) community. Further work will be presented on using decomposition analysis of the dynamic model to identify optimal locations for sensors in order to obtain the quickest and most accurate estimate the true state of the system. Next, this dissertation will address a specific distributed cyber-physical systems problem: coordinated multi-agent weeding of a field of crops under conditions of partial environmental information. In conjunction with developing and adapting the E-GP model for this application, this chapter will address the classic explore/exploit problem of making intelligent agent-allocation decisions in a dynamic environment with limited information. The theory developed in these chapters will help inform the final chapter, in which another challenging application is analyzed and solved: visual search in a large-scale environment using a pan-tilt-zoom (PTZ) camera."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning and decision-making in spatiotemporally varying domains"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish","Mehta, Prashant","Sreenivas, R.S.","Gazzola, Mattia"],"dc:creator":["Whitman, Joshua Earl"],"dc:date":["2023-05","2023-04-26"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Joshua Whitman, accepted the attached license on 2023-04-25 at 01:18.","The student, Joshua Whitman, submitted this Dissertation for approval on 2023-04-25 at 01:31.","This Dissertation was approved for publication on 2023-04-26 at 14:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19154 on 2023-09-01 at 16:55:16","Modeling and monitoring of large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution using sensors that can only partially observe the system is a fundamental problem in many applications. In particular, in order to make intelligent decisions in robot coordination and actuation, the robot(s) need to be able to predict the full state of their evolving environment at any given time, given only sparse measurements. The purpose of this dissertation is to take steps towards addressing this challenging problem. The rapid advances in the computational power of compact systems and robotics as a whole has led to an explosion of real-world applications for such distributed cyber-physical systems. This work will present advances in this domain, both in theory and in experiments. It will begin with a description of the Kernel Observers (KO) approach and the derivation of the Evolving Gaussian Processes (E-GP) model for generalizing across similar spatiotemporally evolving systems. It will be shown that method is effective for modeling nonlinear fluid flows, and that there are indeed deep theoretical connections with Koopman operator theory and Dynamic Mode Decomposition (DMD) which have gained recognition in the Computational Flow Dynamics (CFD) community. Further work will be presented on using decomposition analysis of the dynamic model to identify optimal locations for sensors in order to obtain the quickest and most accurate estimate the true state of the system. Next, this dissertation will address a specific distributed cyber-physical systems problem: coordinated multi-agent weeding of a field of crops under conditions of partial environmental information. In conjunction with developing and adapting the E-GP model for this application, this chapter will address the classic explore/exploit problem of making intelligent agent-allocation decisions in a dynamic environment with limited information. The theory developed in these chapters will help inform the final chapter, in which another challenging application is analyzed and solved: visual search in a large-scale environment using a pan-tilt-zoom (PTZ) camera."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120097"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 by Joshua Earl Whitman. All rights reserved."],"dc:subject":["Machine Learning","Dynamics","Gaussian Process","Computational Flow Dynamics","Traveling Salesman","Machine Vision","Koopman Operator","Agricultural Robotics","Decision And Control"],"dc:title":["Learning and decision-making in spatiotemporally varying domains"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}