This paper describes a set of closely related C++ software tools for manipulating XML (eXtensible Markup Language) schemas and XML instance files and translating them into OWL (Web Ontology Language) class files and OWL instance files. They include: (1) an XML schema parser, (2) an XML instance file parser generator, (3) the instance file parsers generated by the XML instance file parser generator, (4) an XML schema to OWL class generator, (5) a domain instance XML to OWL translator generator, and (6) the domain in­ stance XML to OWL translators generated by the domain instance XML to OWL translator generator. These tools have been applied to information models for kitting environments and kitting plans. The main focus is on the last three tools, which differ significantly from existing resources. The paper also discusses differences between OWL and XML schema that make translation difficult, and how the tools overcome the difficulties. The tools were built at the National Institute of Standards and Technology in support of the Agility Performance of Robotic Systems.

In this article, we present a novel approach to intention recognition, based on the recognition and representation of state information in a cooperative human–robot environment. States are represented by a combination of spatial relations along with cardinal direction information. The output of the Intention Recognition Algorithms will allow a robot to help a human perform a perceived operation or, minimally, not cause an unsafe situation to occur. We compare the results of the Intention Recognition Algorithms to those of an experiment involving human subjects attempting to recognize the same intentions in a manufacturing kitting domain. In almost every case, results show that the Intention Recognition Algorithms performed as well, if not better, than a human performing the same activity.

In this chapter, the authors describe a novel approach for inferring intention during cooperative human-robot activities through the representation and ordering of state information. State relationships are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. The combination of all relevant state relationships at a given point in time constitutes a state. A template matching approach is used to match state relations to known intentions. This approach is applied to a manufacturing kitting operation1, where humans and robots are working together to develop kits. Based upon the sequences of a set of predefined high-level state relationships that must be true for future actions to occur, a robot can use the detailed state information presented in this chapter to infer the probability of subsequent actions. This would enable the robot to better help the human with the operation or, at a minimum, better stay out of his or her way.

The IEEE Robotics and Automation Society's (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.

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.

It should be noted that multiple kits may be built simul taneously.Finished kits are moved to the assembly floor where components are picked from the kit for use in the assembly procedure.The kits are normally designed to facil itate component picking in the correct sequence for assembly.Component orientation may be constrained by the kit design in order to ease the pick-to-assembly process.Empty kits are returned to the kit building area for reuse.

This article presents a newly developed knowledge methodology/model that was designed to support the IEEE Robotics and Automation Society's Ontologies for Robotics and Automation Working Group. This methodology/model allows for the creation of systems that demonstrate flexibility, agility, and the ability to be rapidly re-tasked. The methodology/model will be illustrated through a case study in the area of robotic kit building. Through this case study, the knowledge model will be presented, and automatic tools for optimizing the knowledge representation for planning systems and execution systems will be discussed.

In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human-robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level states relationships that must be true for future actions to occur, a robot can use the detailed state information presented in this paper to infer the probability of subsequent actions occurring. This would enable the robot to better help the human with the operation or, at a minimum, better stay out of his or her way.