Autonomy and Artificial Intelligence Test
Abstract
The UAST project continued test technology development supporting the near term challenges identified in the 2013–2038 DoD Unmanned Systems Integrated Roadmap, such as, integrating DoD unmanned systems within the National Airspace and safely operating unmanned aerial systems within the Major Range and Test Facility Bases (MRTFB). The UAST project collaborated with the Autonomy Community of Interest (COI) Test and Evaluation, Verification and Validation Working Group to ensure that the UAST project is investing in technologies relevant to the future of autonomous systems. The UAST project explored technologies required for T&E of emerging UAS architectures, functional components, and interfaces. The UAST project emphasized autonomy test technologies that can be integrated for use in a Test and Training Enabling Architecture (TENA) environment within the MRTFB. The UAST project continued investments in robustness testing technology to detect and predict vulnerabilities and failures within UAS software. The UAST project continued developments to automatically predict test vehicle collision potentials and cue test range controllers to take corrective action. These technologies will also prevent the test vehicle from violating flight envelopes, range boundaries, and warning areas. The UAST project initiated an effort to develop a software tool that will enable testers to monitor the internal autonomous processing states of a system under test without interfering with its operations or requiring modification to the system’s software or hardware. The UAST project completed efforts that rapidly identify challenging test scenarios for an undersea unmanned vehicle (UUV) under test. The effort identified performance boundaries for autonomy as they relate to the environment, mission, and vehicle state spaces; this technology transitioned to the Naval Undersea Warfare Center-Keyport. The UAST project has initiated development of technology to address the T&E of ground and air autonomy using optimization algorithms to rapidly generate salient test scenarios.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2020
- Source ID
- 13203861a73cc6dfe55233cebb9f5424