Development of a non-contextual model for determining the autonomy level of intelligent unmanned systems

A simple, quantitative measure for encapsulating the autonomous capabilities of unmanned systems (UMS) has yet to be established. Current models for measuring a UMS’s autonomy level require extensive, operational level testing, and provide a means for assessing the autonomy level for a specific mission/task and operational environment. A more elegant technique for quantifying autonomy using component level testing of the robot platform alone, outside of mission and environment contexts, is desirable. Using a high level framework for UMS architectures, such a model for determining a level of autonomy has been developed. The model uses a combination of developmental and component level testing for each aspect of the UMS architecture to define a non-contextual autonomous potential (NCAP). The NCAP provides an autonomy level, ranging from fully non- autonomous to fully autonomous, in the form of a single numeric parameter describing the UMS’s performance capabilities when operating at that level of autonomy.

[1]  James S. Albus,et al.  Autonomy levels for unmanned systems (ALFUS) framework: an update , 2005, SPIE Defense + Commercial Sensing.

[2]  S. Balakirsky,et al.  Towards Quantitative Comparisons of Robot Algorithms : Experiences with SLAM in Simulation and Real World Systems , 2007 .

[3]  Phillip J Durst PERFORMANCE EVALUATION AND BENCHMARKING FOR UNMANNED GROUND VEHICLES , 2010 .

[4]  Patrik Haslum,et al.  A Distributed Architecture for Autonomous Unmanned Aerial Vehicle Experimentation , 2004, DARS.

[5]  Adam Jacoff,et al.  Urban search and rescue robot performance standards: progress update , 2007, SPIE Defense + Commercial Sensing.

[6]  James S. Albus,et al.  4-D/RCS: a reference model architecture for Demo III , 1997, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[7]  Robin R. Murphy,et al.  From remote tool to shared roles , 2008, IEEE Robotics & Automation Magazine.

[8]  Andreas Birk,et al.  Determining Map Quality through an Image Similarity Metric , 2009, RoboCup.

[9]  Adam Jacoff,et al.  Performance measures framework for unmanned systems (PerMFUS): initial perspective , 2009, PerMIS.

[10]  Christopher D. Wickens,et al.  A model for types and levels of human interaction with automation , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[11]  James S. Albus,et al.  4-D/RCS reference model architecture for unmanned ground vehicles , 1999, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[12]  Herman Herman,et al.  Semi autonomous mine detection system , 2010, Defense + Commercial Sensing.

[13]  Massimo Caccia,et al.  Unmanned underwater vehicles for scientific applications and robotics research : The ROMEO project , 2000 .

[14]  Michael A. Goodrich,et al.  Experiments in adjustable autonomy , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[15]  Elena R. Messina,et al.  Hierarchical world model for an autonomous scout vehicle , 2002, SPIE Defense + Commercial Sensing.