Thomas R. Kramer

An industrial robotic knowledge representation for kit building applications

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Industrial Robots sub-group is tasked with studying industrial applications of the ontology. One of the first areas of interest for this subgroup is the area of kit building or kitting. This is a process that brings parts that will be used in assembly operations together in a kit and then moves the kit to the assembly area where the parts are used in the final assembly. This paper examines the knowledge representations that have been developed and implemented for the kitting problem.

The Canonical Robot Command Language (CRCL)

Industrial robots can perform motion with sub-millimeter repeatability when programmed using the teach-and-playback method. While effective, this method requires significant up-front time, tying up the robot and a person during the teaching phase. Off-line programming can be used to generate robot programs, but the accuracy of this method is poor unless supplemented with good calibration to remove systematic errors, feed-forward models to anticipate robot response to loads, and sensing to compensate for unmodeled errors. These increase the complexity and up-front cost of the system, but the payback in the reduction of recurring teach programming time can be worth the effort. This payback especially benefits small-batch, short-turnaround applications typical of small-to-medium enterprises, who need the agility afforded by off-line application development to be competitive against low-cost manual labor. To fully benefit from this agile application tasking model, a common representation of tasks should be used that is understood by all of the resources required for the job: robots, tooling, sensors, and people. This paper describes an information model, the Canonical Robot Command Language (CRCL), which provides a high-level description of robot tasks and associated control and status information.

Functional requirements of a model for kitting plans

Industrial assembly of manufactured products is often performed by first bringing parts together in a kit and then moving the kit to the assembly area where the parts are used to assemble products. Kitting, the process of building kits, has not yet been automated in many industries where automation may be feasible. Consequently, the cost of building kits is higher than it could be. We are addressing this problem by building models of the knowledge that will be required to operate an automated kitting workstation. A first pass has been made at modeling non-executable information about a kitting workstation that will be needed, such as information about a robot, parts, kit designs, grippers, etc. A model (or models) of executable plans for building kits is also needed. The plans will be used by execution systems that control robots and other mechanical devices to build kits. The first steps in building a kitting plan model are to determine what the functional requirements are and what model constructs are needed to enable meeting those requirements. This paper discusses those issues.

Feature-Based Control of a Machining Center

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Performance evaluation of knowledge-based kitting via simulation

The IEEE Robotics and Automation Societys (RAS) Ontologies for Robotics and Automation Working Group is dedicated to developing a knowledge representation for robotics and automation. As part of this working group, the Industrial Robots sub-group is tasked with studying industrial applications of the knowledge representation. One of the first areas of interest for this subgroup is the area of kit building or kitting. This is a process that brings parts that will be used in assembly operations together in a kit and then moves the kit to the area where the parts are used in the final assembly. It is anticipated that utilization of the knowledge representation will allow for the development of higher performing kitting systems. While our previous efforts were aimed at designing the basis for performance methods and metrics that may be utilized to determine the performance of kitting systems, this paper presents a system that evaluates the performance of kitting systems through simulation using specific metrics.

Developing an Activity Model for Selecting Dimensional-Metrology Systems in Inspection Planning

This paper describes an activity model that represents activities and information flow in dimensional metrology systems based on design information and measurement requirements from manufacturers. The purpose of developing the activity model is to facilitate measurement equipment selection rules and conformity decision rules development. The rules can be for users to plan a measurement process using functionally complex and highly capable dimensional measurement equipment and measurement software systems. This activity model provides a basis for developing a rule model as a part of the Quality Information Framework (QIF) standard.

A Simulated Sensor-based Approach for Kit Building Applications

Kit building or kitting is a process in which separate but related items are grouped, packaged, and supplied together as one unit (kit). This paper describes advances in the development of kitting simulation tools that incorporate sensing/control and parts detection capabilities. To pick and place parts and components during kitting, the kitting workcell relies on a simulated sensor system to retrieve the six-degree of freedom (6DOF) pose estimation of each of these objects. While the use of a sensor system allows objects’ poses to be obtained, it also helps detecting failures during the execution of a kitting plan when some of these objects are missing or are not at the expected locations. A simulated kitting system is presented and the approach that is used to task a sensor system to retrieve 6DOF pose estimation of specific objects (objects of interest) is given.