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dc.contributor.advisorAndreas Hofmann and Brian C. Williams.en_US
dc.contributor.authorBurke, Sean Een_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-11-04T21:37:36Z
dc.date.available2014-11-04T21:37:36Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/91450
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.en_US
dc.description5en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 97).en_US
dc.description.abstractRobots are becoming more and more prominent in the world of manufacturing for assembling products. Currently most of these robots, such as the ones used in automobile manufacturing have specific pre-programmed tasks and motions, and no sense of their surrounding environment. In many of today's applications, this method will not be sufficient, as many real world environments are unstructured and could cause disturbances to the robots requiring the motion and task plans to be modified. If a robot has a task to complete, a planner, such as Bidirectional RRT [5], will generate a motion plan to complete the task. If that motion plan becomes infeasible, because the goal has changed, or an obstacle has moved into the robot's path, the robot will need to make an adjustment. One method is to generate a new plan. This can be quite time-consuming, especially since the time is not proportional to the size of the change making re-planning excessive for small adjustments. The problem we would like to solve is adjusting to minor disturbances much faster than re-planning. Re-planning can often take a few seconds, where we would like to make adjusted plans in less than a second. In this thesis, we present a method for solving this problem. We use an incremental adjustment approach that can make minor adjustments in response to collisions or goal changes where the time taken to make adjustments is proportional to the extent of the changes made. To make the adjustments to the plan, we have developed a quadratic program that will make near-optimal adjustments to each robot joint pose in a robot's motion plan based on the goal region and a reaction vector. The goal region is the region the robot manipulator needs to be in to accomplish its task. The reaction vector is a vector that specifies the direction the robot would need to move in order to remove itself from a collision if there is a collision. Along with this quadratic program, we give a method for computing these reaction vectors. These two pieces are the major components of our algorithm and the key innovations made in this thesis. The algorithm allows the robot to make minor adjustments to its plan in an unstructured environment in about a quarter of a second. The adjustments are near optimal, in that they only deviate slightly from the original plan, and are made much faster than traditional planning algorithms. The overall goal is to build a complete robust execution system, and the reactive trajectory adjustment algorithm presented in this thesis is an important piece of the overall system.en_US
dc.description.statementofresponsibilityby Sean E. Burke.en_US
dc.format.extent97 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 trajectory adjustment for motion 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.en_US
dc.identifier.oclc893858102en_US


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