2026-09-20T11:14:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/854992026-06-06T00:48:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Brian C. Williams.
Shroff, Ameya
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-06T15:46:26Z
2014-03-06T15:46:26Z
2013
2013
http://hdl.handle.net/1721.1/85499
871002514
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-100).
We 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.
by Ameya Shroff.
M.Eng.
100 pages
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
Electrical Engineering and Computer Science.
Reactive integrated motion planning and execution using Chekhov
Thesis
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<Abstract>We 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.</Abstract>
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