David J. Atkinson

Enhancing aerospace systems autonomy through predictive monitoring

The quick and reliable detection of anomalous behavior in aerospace systems is addressed. Ways to adjust nominal sensor value expectations dynamically, taking into account the changing operating context of the system, are considered. How to utilize sensors selectively, determining which subset of the available sensors to use at any given time to verify nominal operation efficiently without processing a prohibitive amount of data, is examined.

Methodology for study of human-robot social interaction in dangerous situations

Applications of robotics in dangerous domains such as search and rescue require new methodology for study of human-robot interaction. Perceived danger evokes unique human psycho-physiological factors that influence perception, cognition and behavior. Human first responders are trained for victim psychology. Apart from real-life instances of disasters, studies of robots in this environment are difficult to perform safely and systematically with sufficient controls, fidelity, and in a manner that permits exact replication. Consequently, the trend to deploy rescue robots, for example, is proceeding largely without benefit of knowing whether human victims will readily cooperate with robot rescuers. The capability to deal with unique victim psychology has not been a testable requirement. We report on the methodology of an on-going study that uses virtual reality to provide a feature-rich immersive environment that is sufficient to evoke fear-related psychological response, provides simulation capability for robots, and enables systematic study trials with automated data collection via an embedded scripting language. The methodology presented provides an effective way to study human interaction with intelligent agents embodied as robots in application domains that would otherwise be impossible in the real world.

SHARP: Automated Monitoring of Spacecraft Health and Status*

This paper briefly describes the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory and highlights some difficulties associated with the existing technology used in mission operations. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The SHARP system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in a realtime operations setting during the Voyager spacecraft encounter with Neptune in August, 1989. The SHARP system will be delivered to the JPL Space Flight Operations Center for regular use by planetary flight projects, including the Galileo and Magellan spacecraft, and will also be applied to monitoring and control applications in the Deep Space Networks Network Operations Control Center.