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dc.contributor.advisorBrian C. Williams.en_US
dc.contributor.authorShroff, Ameyaen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-03-06T15:46:26Z
dc.date.available2014-03-06T15:46:26Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/85499
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 99-100).en_US
dc.description.abstractWe envision a world in which robots and humans can collaborate to perform complex tasks in real-world environments. Current motion planners successfully generate trajectories for a robot with multiple degrees of freedom, in a cluttered environment, and ensure that the robot can achieve its goal while avoiding all the obstacles in the environment. However, these planners are not practical in real world scenarios that involve unstructured, dynamic environments for a three primary reasons. First, these motion planners assume that the environment the robot is functioning in, is well-known and static, both during plan generation and plan execution. Second, these planners do not support temporal constraints, which are crucial for planning in a rapidly-changing environment and for allowing task synchronisation between the robot and other agents, like a human or even another robot. Third, the current planners do not adequately represent the requirements of the task. They often over-constrain the task description and are hence unable to take advantage of task flexibility which may aid in optimising energy efficiency or robustness. In this thesis we present Chekhov, a reactive, integrated motion planning and execution executive that addresses these shortcomings using four key innovations. First, unlike traditional planners, the planning and execution components of Chekhov are very closely integrated. This close coupling blurs the traditional, sharp boundary between the two components and allows for optimal collaboration. Second, Chekhov represents temporal constraints, which allows it to perform operations that are temporally synchronised with external events. Third, Chekhov uses an incremental search algorithm which allows it to rapidly generate a new plan if a disturbance is encountered that threatens the execution of the existing plan. Finally, unlike standard planners which generate a single reference trajectory from the start pose to the goal pose, Chekhov generates a Qualitative Control Plan using Flow Tubes that represent families of feasible trajectories and associated control policies. These flow tubes provide Chekhov with a flexibility that is extremely valuable and serve as Chekhov's first line of defence.en_US
dc.description.statementofresponsibilityby Ameya Shroff.en_US
dc.format.extent100 pagesen_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.subjectElectrical Engineering and Computer Science.en_US
dc.titleReactive integrated motion planning and execution using Chekhoven_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc871002514en_US


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