Autonomous Robotic Escort Incorporating Motion Prediction with Human Intention

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dc.contributor.authorConte, Dean Edwarden
dc.contributor.committeechairFurukawa, Tomonarien
dc.contributor.committeememberAkbari Hamed, Kavehen
dc.contributor.committeememberAsbeck, Alan T.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2021-03-03T09:00:22Zen
dc.date.available2021-03-03T09:00:22Zen
dc.date.issued2021-03-02en
dc.description.abstractThis thesis presents a framework for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses accurate path prediction incorporating human intention to locate the robot in front of the human while walking. Human intention is inferred by the head pose, an effective past-proven implicit indicator of intention, and fused with conventional physics-based motion prediction. The human trajectory is estimated and predicted using a particle filter because of the human's nonlinear and non-Gaussian behavior, and the robot control action is determined from the predicted human pose allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an omnidirectional mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate.en
dc.description.abstractgeneralThis thesis presents a method for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses human intention to predict the walk path allowing the robot to be in front of the human while walking. Human intention is inferred by the head direction, an effective past-proven indicator of intention, and is combined with conventional motion prediction. The robot motion is then determined from the predicted human position allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate. The unique escorting interaction method proposed has applications such as touch-less shopping cart robots, exercise companions, collaborative rescue robots, and sanitary transportation for hospitals.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:29295en
dc.identifier.urihttp://hdl.handle.net/10919/102581en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectIntention recognitionen
dc.subjectautonomous agentsen
dc.subjecthuman trackingen
dc.subjecthuman robot interactionen
dc.subjectparticle filteren
dc.titleAutonomous Robotic Escort Incorporating Motion Prediction with Human Intentionen
dc.typeThesisen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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