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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 state relationships that must be true for future actions to occur, a robot can use the detailed state information described in this paper to infer the probability of subsequent actions occurring. This would allow the robot to better help the human with the task or, at a minimum, better stay out of his or her way.

The attendees of the Knowledge Representation and Ontologies for Autonomous Systems Symposium 1 applied their collective intelligence in an attempt to solve one of the universe's great challenges: how do you autonomously collect garbage from an airport facility? The attendees divided into three pre-selected, cross-disciplinary groups that were headed by Stephen Balakirsky (NIST), Elena Messina (NIST), and Scott Smith (Boeing). The teams were given limited direction and 3 h to develop a solution to the problem. Results from the groups were briefed the following morning at a plenary session.

We report the results of a first implementation demonstrating the use of an ontology to support reasoning about obstacles to improve the capabilities and performance of on-board route planning for autonomous vehicles. This is part of an overall effort to evaluate the performance of ontologies in different components of an autonomous vehicle within the 4D/RCS system architecture developed at NIST. Our initial focus has been on simple roadway driving scenarios where the controlled vehicle encounters potential obstacles in its path. As reported elsewhere [C. Schlenoff, S. Balakirsky, M. Uschold, R. Provine, S. Smith, Using ontologies to aid navigation planning in autonomous vehicles, Knowledge Engineering Review 18 (3) (2004) 243–255], our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to plan to avoid the object. We describe the results of the first implementation that integrates the ontology, the reasoner and the planner. We describe our insights and lessons learned and discuss resulting changes to our approach.

This paper presents a cost-based adaptive planning agent and knowledge layers that is operating at one level of a deliberative hierarchical planning system for autonomous road driving. At this level, the planning agent is responsible for developing fundamental driving maneuvers that allow a vehicle to travel safely amongst moving and stationary objects. This is facilitated through the application of knowledge to the graph creation process and the use of dynamic cost function within the incrementally created planning graph. The cost function varies to comply with particular road, regional, or event driven situations, and when coupled with the incremental graph expansion allows for the agent to implement hard and soft system constraints. Further discussion will be provided that details one of the expert systems that is implemented to provide the planning system with this knowledge in real-time.