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Karl lagnemma. Kewlani, Gaurav Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2010-05-25T21:13:14Z 2010-05-25T21:13:14Z 2009 2009 http://hdl.handle.net/1721.1/55270 613216740 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 107-111). The ability of autonomous or semi-autonomous unmanned ground vehicles (UGVs) to rapidly and accurately predict terrain negotiability, generate efficient paths online and have effective motion control is a critical requirement for their safety and use in unstructured environments. Most techniques and algorithms for performing these functions, however, assume precise knowledge of vehicle and/or environmental (i.e. terrain) properties. In practical applications, significant uncertainties are associated with the estimation of the vehicle and/or terrain parameters, and these uncertainties must be considered while performing the above tasks. Here, computationally inexpensive methods based on the polynomial chaos approach are studied that consider imprecise knowledge of vehicle and/or terrain parameters while analyzing UGV dynamics and mobility, evaluating safe, traceable paths to be followed and controlling the vehicle motion. Conventional Monte Carlo methods, that are relatively more computationally expensive, are also briefly studied and used as a reference for evaluating the computational efficiency and accuracy of results from the polynomial chaos-based techniques. by Gaurav Kewlani. S.M. 112 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Mechanical Engineering. Stochastic approaches to mobility prediction, path planning and motion control for ground vehicles in uncertain environments Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="cbbf4b23-0f24-4cc9-8ad7-ea5c4c57aae6"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Stochastic approaches to mobility prediction, path planning and motion control for ground vehicles in uncertain environments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Kewlani, Gaurav</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <Abstract>The ability of autonomous or semi-autonomous unmanned ground vehicles (UGVs) to rapidly and accurately predict terrain negotiability, generate efficient paths online and have effective motion control is a critical requirement for their safety and use in unstructured environments. Most techniques and algorithms for performing these functions, however, assume precise knowledge of vehicle and/or environmental (i.e. terrain) properties. In practical applications, significant uncertainties are associated with the estimation of the vehicle and/or terrain parameters, and these uncertainties must be considered while performing the above tasks. Here, computationally inexpensive methods based on the polynomial chaos approach are studied that consider imprecise knowledge of vehicle and/or terrain parameters while analyzing UGV dynamics and mobility, evaluating safe, traceable paths to be followed and controlling the vehicle motion. Conventional Monte Carlo methods, that are relatively more computationally expensive, are also briefly studied and used as a reference for evaluating the computational efficiency and accuracy of results from the polynomial chaos-based techniques.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>