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Proceedings Paper

Using artificial intelligence for automating testing of a resident space object collision avoidance system on an orbital spacecraft
Author(s): Jeremy Straub
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Paper Abstract

Resident space objects (RSOs) pose a significant threat to orbital assets. Due to high relative velocities, even a small RSO can cause significant damage to an object that it strikes. Worse, in many cases a collision may create numerous additional RSOs, if the impacted object shatters apart. These new RSOs will have heterogeneous mass, size and orbital characteristics. Collision avoidance systems (CASs) are used to maneuver spacecraft out of the path of RSOs to prevent these impacts. A RSO CAS must be validated to ensure that it is able to perform effectively given a virtually unlimited number of strike scenarios. This paper presents work on the creation of a testing environment and AI testing routine that can be utilized to perform verification and validation activities for cyber-physical systems. It reviews prior work on automated and autonomous testing. Comparative performance (relative to the performance of a human tester) is discussed.

Paper Details

Date Published: 13 June 2014
PDF: 5 pages
Proc. SPIE 9095, Modeling and Simulation for Defense Systems and Applications IX, 909502 (13 June 2014); doi: 10.1117/12.2049499
Show Author Affiliations
Jeremy Straub, The Univ. of North Dakota (United States)


Published in SPIE Proceedings Vol. 9095:
Modeling and Simulation for Defense Systems and Applications IX
Eric J. Kelmelis, Editor(s)

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