Real-Time Computational Scheduling with Path Planning for Autonomous Mobile Robots

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dc.contributor.authorChen, David Xitaien
dc.contributor.committeechairWilliams, Ryan K.en
dc.contributor.committeememberZeng, Haiboen
dc.contributor.committeememberDoan, Thinh Thanhen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2024-06-06T08:02:46Zen
dc.date.available2024-06-06T08:02:46Zen
dc.date.issued2024-06-05en
dc.description.abstractWith the advancement in technology, modern autonomous vehicles are required to perform more complex tasks and navigate through challenging terrains. Thus, the amount of computation resources to accurately accomplish those tasks have exponentially grown in the last decade. With growing computational intensity and limited computational resources on embedded devices, schedulers are necessary to manage and fully optimize computational loads between the GPU and CPU as well as reducing the power consumption to maximize time in the field. Thus far, it has been proven the effectiveness of schedulers and path planners on computational load on embedded devices through numerous bench testing and simulated environments. However, there have not been any significant data collection in the real-world with all hardware and software combined. This thesis focuses on the implementation of various computational loads (i.e. scheduler, path planner, RGB-D camera, object detection, depth estimation, etc.) on the NVIDIA Jetson AGX Xavier and real-world experimentation on the Clearpath Robotics Jackal. We compare the computation response time and effectiveness of all systems tested in the real-world versus the same software and hardware architecture on the bench.en
dc.description.abstractgeneralModern autonomous vehicles are required to perform more complex tasks with limited computational resources, power and operating frequency. In recent past, the research around autonomous vehicles have been focused on proving the effectiveness of using software-based programming on embedded devices with integrated GPU to improve the overall performance by speeding up task completion. Our goal is to perform real-world data collection and experimentation with both hardware and software frameworks onboard the Clearpath Robotics Jackal. This will validate the efficiency and computational load of the software framework under multiple varying environments.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:40656en
dc.identifier.urihttps://hdl.handle.net/10919/119318en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRobot computationen
dc.subjectReal-time schedulingen
dc.subjectSensorsen
dc.subjectEmbedded systemsen
dc.subjectSLAMen
dc.subjectRobot operating systemen
dc.subjectSafetyen
dc.subjectPerformanceen
dc.titleReal-Time Computational Scheduling with Path Planning for Autonomous Mobile Robotsen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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