Task-based decision making and control of robotic manipulators

dc.contributor.advisorTesar, Delberten
dc.contributor.advisorKapoor, Chetanen
dc.creatorPholsiri, Chalongrathen
dc.date.accessioned2008-08-28T21:58:36Zen
dc.date.available2008-08-28T21:58:36Zen
dc.date.issued2004en
dc.description.abstractRedundancy affords an opportunity to deploy robotic manipulators in a broader variety of tasks owing to extra system resources. Decision making is a procedure that manages these extra resources and utilizes them in a meaningful fashion. Decision making is inherently complex and this complexity is a major hindrance to wider acceptance of redundant manipulators in various robotic applications. This report aims directly at decreasing this complexity by incorporating the concept of task requirements as an integral part of the decision making process. Most traditional Redundancy Resolution Techniques (RRTs) place an undue burden on the user by forcing him to choose a set of performance criteria and assign relative importance to each criterion for a given task. The Task-Based Redundancy Resolution (TBRR) approach presented here changes all that by directly utilizing robotic task requirements in terms of speed, force, and accuracy in the decision making process itself. TBRR searches the null space for configurations that comply with system constraints such as joint travel limits, singularities, and obstacles. TBRR then determines configurations that satisfy task requirements by estimating in real-time robot capabilities using a newly-developed technique called the Vector Expansion method. Finally, TBRR selects, among these configurations, the best solution according to efficiency or any other desired criterion. As a result, TBRR does not require a confusing chore of criteria fusion and thus is easier to use than traditional RRTs. Demonstrations on three geometrically different (6-, 7-, and 10-DOF) spatial robots show that TBRR provides 3% to 147% improvement over traditional RRTs as far as satisfying task requirements. A preliminary effort at integrating a learning method to help determine proper values of subjective parameters significantly reduces the human trial and error effort while showing a 3% improvement over the best set of hand-tuned parameters. Another issue that has not been adequately addressed but is critical to redundant robotic manipulation is force control. Contact tasks, which represent a large portion of robotic tasks, cannot be effectively performed without one form of force control or another. This report illustrates the feasibility of integrating existing force control methods with the TBRR approach. The end result of this research is a task-based decision making and control framework that should enhance task performance of robotic manipulators operating with redundancy in a wider range of robotic tasks.
dc.description.departmentMechanical Engineeringen
dc.format.mediumelectronicen
dc.identifierb59340514en
dc.identifier.oclc58389490en
dc.identifier.proqst3150712en
dc.identifier.urihttp://hdl.handle.net/2152/1388en
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subject.lcshRoboticsen
dc.subject.lcshManipulators (Mechanism)en
dc.titleTask-based decision making and control of robotic manipulatorsen
dc.type.genreThesisen
thesis.degree.departmentMechanical Engineeringen
thesis.degree.disciplineMechanical Engineering.en
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen

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