Cited By
View all- Khamvilai TDunham JFeron EJohnson E(2021)Avionics of Aerial RobotsCurrent Robotics Reports10.1007/s43154-021-00051-8Online publication date: 19-Apr-2021
Towards DO-178C certification of adaptive learning UAV agents designed with a cognitive architecture
Pages 174 - 177
Abstract
Adaptive and Learning Agents (ALAs) bring computational intelligence to their Cyber Physical host systems to adapt to novel situations encountered in their complex operational environment. They do so by learning from their experience to improve their performance. RTCA DO-178C specifies a stringent certification process for airborne software which represents several challenges when applied to an ALA in regards of functional completeness, functional correctness, testability and adaptability. This research claims that it is possible to certify an Adaptive Learning Unmanned Aerial Vehicle (UAV) Agent designed as per a Cognitive Architecture with current DO-178C certification process when leveraging a qualified tool (DO-330), Model-Based Development and Verification (DO-331) and Formal Methods (DO-333). The research consists in developing, as a case study, an ALA embedded in a UAV aimed at neutralizing rogue UAVs in the vicinity of civil airports and test it in the field. This article is the plan to complete, by end 2022, a dissertation currently in its confirmation phase.
References
[1]
Radio Technical Commission for Aeronautics (RTCA), 2011. DO-178C Software Considerations in Airborne Systems and Equipment Certification (Dec, 2011).
[2]
Siddahartha Bhattacharyya, Darren Cofer, David J. Musliner, Joseph Muller and Eric Engstrom, 2015. Certification Considerations for Adaptive Systems. NASA Langley Research Center, NASA/CR-2015-218702 (Mar, 2015).
[3]
John Pyrgies, Daniel Gigan and Rob Haelterman, 2017. An innovative approach for achieving DO-178C certification of an intelligent system implementing sense-and-avoid function in UAVs. Air Transport Research Society World Conference (Jul, 2017).
[4]
American Institute of Aeronautics and Astronautics (AIAA) Intelligent Systems Technical Committee (ISTC), 2016. Roadmap for Intelligent Systems in Aerospace (Jun, 2016).
[5]
B. Coelho, P. Marzocca, and X. Li, 2019. Proposed workflow to allow artificial intelligent agents for airborne systems and equipment certification. AIAC18: 18th Australian International Aerospace Congress (2019).
[6]
Radio Technical Commission for Aeronautics (RTCA), 2011. DO-330 - Software Tool Qualification Considerations (Dec, 2011).
[7]
Thierry Le Sergent, Alain Le Guennec, François Terrier, Yann Tanguy and Sébastien Gérard, 2012. SCADE System, a comprehensive toolset for smooth transition from Model-Based System Engineering to certified embedded control and display software. ERTS2012: Embedded Real Time Software and Systems (Feb, 2012).
[8]
Jean-Louis Colaço, Bruno Pagano and Marc Pouzet, 2012. Scade 6: A Formal Language for Embedded Critical Software Development. TASE 2017: 11th International Symposium on Theoretical Aspects of Software Engineering (Sep, 2017).
[9]
Radio Technical Commission for Aeronautics (RTCA), 2011. DO-331 - Model Based Development and Verification Supplement to DO-178C and DO-278A (Dec, 2011).
[10]
Ji Zhang and Betty Cheng, 2006. Model-based development of dynamically adaptive software. ICSE 2006: International Conference on Software Engineering (Jan, 2006).
[11]
Radio Technical Commission for Aeronautics (RTCA), 2011. DO-333 - Formal Methods Supplement to DO-178C and DO-278A (Dec, 2011).
[12]
Matt Webster, Michael Fisher, Neil Cameron and Mike Jump, 2011. Formal Methods for the Certification of Autonomous Unmanned Aircraft Systems. 30th International Conference on Computer Safety, Reliability and Security (Sep, 2011).
[13]
Hui-Qing Chong, Ah-Hwee Tan and Gee-Wah Ng, 2007. Integrated cognitive architectures: a survey. Artificial Intelligence Review (Aug, 2007). 28:103.
[14]
Kristin R. Thorisson and Helgi P. Helgason, 2012. Cognitive Architectures and Autonomy: A Comparative Review. Journal of Artificial General Intelligence (Jan, 2012).
[15]
John Pyrgies, 2019. The UAVs Threat to Airport Security: Risk Analysis and Mitigation. Journal of Airline and Airport Management (May, 2012).
[16]
John E. Laird, 2012. The SOAR Cognitive Architecture. MIT press (2012).
Index Terms
- Towards DO-178C certification of adaptive learning UAV agents designed with a cognitive architecture
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View all- Khamvilai TDunham JFeron EJohnson E(2021)Avionics of Aerial RobotsCurrent Robotics Reports10.1007/s43154-021-00051-8Online publication date: 19-Apr-2021
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