Tony Barbera

How task analysis can be used to derive and organize the knowledge for the control of autonomous vehicles

Abstract The real-time control system (RCS) methodology has evolved over a number of years as a technique to capture task knowledge and organize it in a framework conducive to implementation in computer control systems. The fundamental premise of this methodology is that the present state of the task activities sets the context that identifies the requirements for all the support processing. In particular, the task context at any time determines what is to be sensed in the world, what world model states are to be evaluated, which situations are to be analyzed, what plans should be invoked, and which behavior generation knowledge is to be accessed. This results in a methodology that concentrates first and foremost on the task definition. It starts with the definition of the task knowledge in the form of a decision tree that clearly represents the branching of tasks into layers of simpler and simpler subtask sequences. This task decomposition framework is then used to guide the search for and to emplace all of the additional knowledge. This paper explores this process in some detail, showing how this knowledge is represented in a task context-sensitive relationship that supports the very complex real-time processing the computer control systems will have to do.

Simulation and Implementation of an Open Architecture Controller

The National Institute of Standards and Technology has developed a modular definition of components for machine control, and a specification to their interfaces, with broad application to robots, machine tools, and coordinate measuring machines. These components include individual axis control, coordinate trajectory generation, discrete input/output, language interpretation, and task planning and execution. The intent of the specification is to support interoperability of components provided by independent vendors. NIST has installed a machine tool controller based on these interfaces on a 4-axis horizontal machining center at the Pontiac Powertrain Division of General Motors. The intent of this system is to validate that the interfaces are comprehensive enough to serve a demanding application, and to demonstrate several key concepts of open architecture controllers: component interoperability, controller scalability, and function extension. In particular, the GM-NIST Enhanced Machine Controller demonstrates interoperability of motion control hardware, scalability across computing platforms, and extensibility via user-defined graphical user interfaces. An important benefit of platform scalability is the ease with which the developers could test the controller in simulation before site installation. The EMC specifications are serving a larger goal of driving the development of true industry standards that will ultimately benefit users of machine tools, robots, and coordinate measuring machines. To this end, a consortium has been established and cooperative participation with the Department of Energy TEAM program and the US Air Force Title III program has been undertaken.

A hierarchical, multi-resolutional moving object prediction approach for autonomous on-road driving

In this paper, we present a hierarchical multi-resolutional approach for moving object prediction via estimation-theoretic and situation-based probabilistic techniques. The results of the prediction are made available to a planner to allow it to make accurate plans in the presence of a dynamic environment. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort. Experimental results are shown in two simulation environments.

An intelligent ground vehicle ontology for multi-agent system integration

The level of automation in ground combat vehicles being developed for the Armys objective force is greatly increased over the Armys legacy force. The development of these intelligent ground vehicles (IGV) requires a thorough understanding of all of the intelligent behavior that needs to be exhibited by the system so that designers can allocate functionality to humans and/or machines. In this paper, we describe the joint effort currently being performed by DCS Corporation and NIST to develop an intelligent ground vehicle (IGV) ontology using Protege. The goal of this effort is to develop a common, implementation-independent, extendable knowledge source for researchers and developers in the intelligent vehicle community. This paper describes the methodology we have used to identify knowledge in this domain and an approach to capture and visualize the knowledge in the ontology.

A standard intelligent system ontology

The level of automation in combat vehicles being developed for the Armys objective force is greatly increased over the Armys legacy force. This automation is taking many forms in emerging vehicles; varying from operator decision aides to fully autonomous unmanned systems. The development of these intelligent vehicles requires a thorough understanding of all of the intelligent behavior that needs to be exhibited by the system so that designers can allocate functionality to humans and/or machines. Traditional system specification techniques focused heavily on the functional description of the major systems and implicitly assumed that a well-trained crew would operate these systems in a manner to accomplish the tactical mission assigned to the vehicle. In order to allocate some or all of these intelligent behaviors to machines in future vehicles it is necessary to be able to identify and describe these intelligent behaviors in detail. In this paper, we describe an effort to develop an intelligent systems (IS) ontology using Protege. The goal of this effort is to develop a common, implementation-independent, extendable knowledge source for researchers and developers in the intelligent vehicle community that will: * Provide a standard set of domain concepts along with their attributes and inter-relations * Allow for knowledge capture and reuse * Facilitate systems specification, design, and integration, and * Accelerate research in the field. This paper describes the methodology we have used to identify knowledge in this domain and an approach to capture and visualize the knowledge in the ontology.

Fusing disparate information within the 4D/RCS architecture

In this paper, we show how the 4D/RCS (real-time control system) architecture incorporates and integrates multiple types of disparate information into a common, unifying framework. 4D/RCS is based on the supposition that different information modeling techniques offer different advantages. 4D/RCS allows for the capability to capture information in formalisms and at levels of abstraction that are suitable for the way that they are expected to be used. In the context of applying the 4D/RCS architecture to achieving the ultimate goal of the control of autonomous ground vehicle navigation, we describe the procedural and declarative types of information that have been developed and their respective values towards achieving the goal. Experimental results of information fusion within the 4D/RCS architecture are presented from the Demo III experimental unmanned vehicles (XUVs) in an extended series of demonstrations and field tests.