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

Published

December 31, 2004

Author(s)

Tony Barbera, James S. Albus, Elena R. Messina, Craig I. Schlenoff, John A. Horst

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.

Citation

Journal of Robotics & Autonomous Systems

Pub Type

Journals

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Keywords

4D/RCS, Knowledge Engineering, knowledge requirements, methodology, Mobility, Performance Metrics, task decomposition, Unmanned Systems

Citation

Barbera, T. , Albus, J. , Messina, E. , Schlenoff, C. and Horst, J. (2004), How task analysis can be used to derive and organize the knowledge for the control of autonomous vehicles, Journal of Robotics & Autonomous Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822606 (Accessed May 18, 2024)