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Anomaly Detection, Active Learning, Precursor Identification,and Human Knowledge for Autonomous System Safety

The project Autonomy Teaming and TRajectories for ComplexTrusted Operational Reliability (ATTRACTOR) researched and developed Artificial Intelligence with application to multi-Unmanned Aerial Systems (UAS) missions. Such missions, like other complex systems-of-systems, are likely to have previously-unknown, safety relevant anomalies occur due to many possible factors including system failures or degradations, emergent behavior, changes in the environment in which the systems operate, changes in the way the systems are operated. We discuss the application of anomaly detection, active learning, and precursor identification to identify such anomalies and the conditions under which they are more likely to appear. We demonstrate results on simulated multi-UAS missions that show promise to be applied to real missions.

machine learning

Testing Autonomous Systems for Deep Space Exploration

NASA is moving into an era of increasing spacecraft autonomy. However, before autonomy can be routinely utilized, we must provide techniques for providing assurance that the system will perform correctly in flight.

Deep

Autonomous Flight Safety System

Autonomous Flight Safety System (AFSS) is an independent flight safety system designed for small to medium sized expendable launch vehicles launching from or needing range safety protection while overlying relatively remote locations. AFSS replaces the need for a man-in-the-loop to make decisions for flight termination. AFSS could also serve as the prototype for an autonomous manned flight crew escape advisory system. AFSS utilizes onboard sensors and processors to emulate the human decision-making process using rule-based software logic and can dramatically reduce safety response time during critical launch phases. The Range Safety flight path nominal trajectory, its deviation allowances, limit zones and other flight safety rules are stored in the onboard computers. Position, velocity and attitude data obtained from onboard global positioning system (GPS) and inertial navigation system (INS) sensors are compared with these rules to determine the appropriate action to ensure that people and property are not jeopardized. The final system will be fully redundant and independent with multiple processors, sensors, and dead man switches to prevent inadvertent flight termination. AFSS is currently in Phase III which includes updated algorithms, integrated GPS/INS sensors, large scale simulation testing and initial aircraft flight testing.

Ferrell, Bob

Control problems in Autonomous Life Support Systems

Autonomous Life Support Systems (ALSS) are envisioned for long range permanence in space. ALSS would require little or no input of matter for extended periods of time. The design of such a system involves an understanding of both ecological principles and control theory of nonlinear, ill-defined systems. A distinction is drawn between ecosystem survival strategies and the aims of control theory. Experimental work is under way to help combine the two approaches.

Colombano, S. P.