An approach to reducing parameter uncertainty for robotic surface assembly tasks
- UNCC Author/Contributor (non-UNCC co-authors, if there are any, appear on document)
- Amar Saric (Creator)
- Institution
- The University of North Carolina at Charlotte (UNCC )
- Web Site: http://library.uncc.edu/
- Advisor
- Jing Xiao
Abstract: In contrast to hard automation, which relies on the precise knowledge of all parameters and special-purpose machinery, the goal of flexible assembly is to overcome the inherent uncertainty in the location of the parts. The main result of this dissertation is that, for rigid, non-deformable objects, more accurate estimates of parameters, which describe their position and orientation in Cartesian space, can be obtained through active part interaction and estimation using numerical methods. If the objects have large polyhedral or convex features, the parameter estimation problem can be recasting terms of fi tting the collected empirical data to a suitable geometrical model. The planning and execution steps are treated as conceptually separate from the estimation.Additionally, an algorithm for automatic conversion of a compliant path from the Cartesian to the joint space of a general-purpose, 7 degrees of freedom robotic arm is described. This allows for the assembly strategies to be planned in terms of objects'topological features in the task frame. A `back-drivable' Barrett WAM robotic arm without a force sensor was used in all experiments, and approximate compliant motion was achieved by relying on torque limits and impedance. Consequently, the primary focus is on planning, control, and assembly without force sensing. The underlying concepts, however, are much more general and could be extended to incorporate force feedback. Grasping is outside of the scope of this work, and it is assumed throughout that one of the parts is rigidly attached to the end-effector of the robot.
An approach to reducing parameter uncertainty for robotic surface assembly tasks
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Created on 8/3/2017
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Additional Information
- Publication
- Dissertation
- Language: English
- Date: 2017