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dc.contributor.advisorKarl Iagnemma.en_US
dc.contributor.authorPeters, Steven C. (Steven Conrad)en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2012-04-26T18:51:52Z
dc.date.available2012-04-26T18:51:52Z
dc.date.copyright2012en_US
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/70421
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 124-128).en_US
dc.description.abstractHazard avoidance is an important capability for safe operation of robotic vehicles at high speed. It is also an important consideration for passenger vehicle safety, as thousands are killed each year in passenger vehicle accidents caused by driver error. Even when hazard locations are known, high-speed hazard avoidance presents challenges in real-time motion planning and control of nonlinear and potentially unstable vehicle dynamics. This thesis presents methods for planning and control of optimal hazard avoidance maneuvers for a bicycle model with front-wheel steering and wheel slip. The planning problem is posed as an optimization problem in which constrained dynamic quantities, such as friction circle utilization, are minimized, while ensuring a minimum clearance from hazards. These optimal trajectories can be computed numerically, though real-time computation requires simple models and constraints. To simplify the computation of optimal avoidance trajectories, analytical solutions to the optimal planning problem are presented for a point mass subject to an acceleration magnitude constraint, which is analogous to a tire friction circle constraint. The optimal point mass solutions are extended to a nonlinear bicycle model by defining a flatness-based trajectory tracking controller using tire force control. This controller decouples the bicycle dynamics into a point mass at the front center of oscillation with an additional degree of freedom related to the vehicle yaw dynamics. Structure is identified in the yaw dynamics and is exploited to characterize stability limits. Simulation results verify the stability properties of the yaw dynamics. These results were applied to a semi-autonomous driver assistance system and demonstrated experimentally on a full-sized passenger vehicle. Efficient computation of point mass avoidance maneuvers was used as a cost-to-go for real-time numerical optimization of trajectories for a bicycle model. The experimental system switches control authority between the driver and an automatic avoidance controller so that the driver retains control authority in benign situations, and the automatic controller avoids hazards automatically in hazardous situations.en_US
dc.description.statementofresponsibilityby Steven C. Peters.en_US
dc.format.extent135 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleOptimal planning and control for hazard avoidance of front-wheel steered ground vehiclesen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc785181619en_US


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