{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120126"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120126","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Toward dynamically scalable open-source motion planning on the mobile edge and in the cloud","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'. 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The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Emmanuel Gallegos, accepted the attached license on 2023-04-27 at 13:17.","The student, Emmanuel Gallegos, submitted this Thesis for approval on 2023-04-27 at 13:36.","This Thesis was approved for publication on 2023-05-01 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19214 on 2023-09-01 at 16:55:42","This work presents a platform capable of deploying massively scalable and portable high performance software for robotics applications by utilizing containerization technologies. To this end, we employ a fully automated method for creating a swarm of containers among machines with homogeneous or heterogeneous computing power and operating systems that can work in tandem to perform large-scale distributed computation, including the ability to leverage robots as mobile edge devices. We demonstrate that this method can also be massively scaled on a large, shared HPC cluster, and evaluate the overhead associated with containerization by performing highly distributed motion planning calculations. For each of these platforms, we compare bare-metal and containerized runtime and scalability and show that our containerized platform is capable of fully utilizing the resources of the host machines while achieving the same nearly-linear scalability for parallel sampling-based motion planning algorithms that is possible on bare-metal implementations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Toward dynamically scalable open-source motion planning on the mobile edge and in the cloud"]}]}],"canonical_facts":{"dc:contributor":["Amato, Nancy M"],"dc:creator":["Gallegos, Emmanuel"],"dc:date":["2023-05","2023-05-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. 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