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.

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.

We have developed PRIDE (prediction in dynamic environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE incorporates a long-term (LT) prediction approach based on situation recognition and a short-term (ST) prediction approach based on vehicle models. These two approaches provide for the prediction of the future location of moving objects at various levels of resolution at the frequency and level of abstraction necessary for planners at different levels within the hierarchy using sensory data. In this paper, we demonstrate the ability to use the results of the short-term prediction algorithms to strengthen/weaken the estimates of the long-term prediction algorithms via experimental results. We provide experimental results in an autonomous on-road driving scenario using AutoSim, a high-fidelity simulation tool that models details about road networks, including individual lanes, lane markings, intersections, legal intersection traversability, etc

In this paper, we present PRIDE (prediction in dynamic environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (real-time control system) architecture and provides information to planners at the level of granularity that is appropriate for their planning horizon. The primary contribution of this paper is the approach that is used to provide a more synergistic framework, in which the results from one prediction algorithm are used to strengthen/weaken the results of another prediction algorithm. In particular, we describe how the results of an estimation-theoretic short-term (ST) prediction algorithm are used to validate the results of a situation-based long-term (LT) prediction algorithm. We show the results of the long-term prediction both before and after the results of the short-term predictions are integrated, and demonstrate how the latter provide better predictions of vehicle future positions using the AutoSim simulation package