Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles, connected through a decentralized network. Significant human-automation collaboration will be needed due to automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing, multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation, in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation"s suggested prompts for new plan consideration, as well as negative attitudes towards unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation"s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.

On March 8, 2007, the U.S. Air Force Academy launched FalconSAT 3, a complex small satellite with three plasma-researching payloads supporting Department of Defense funded science missions. This is the first three-axis stabilized satellite designed, tested, and built by cadets. FalconSAT 3 is equipped with two types of satellite attitude sensors: magnetometers and sun sensors. However, a software issue inhibits the use of sun sensors, so development of a new method of attitude determination was required. The three-axis magnetometers would need to provide the means to accurately depict the spacecraft's orientation at any given time. Because of the ambiguity involved with using a single form of spacecraft attitude information, Extended Kalman Filtering became a viable solution that would combine insufficient data with a mathematical model of satellite motion to achieve accurate attitude estimation. First, a single-axis Kalman Filter was created for determining the yaw axis, assuming roll and pitch remain zero due to gravity-gradient stabilization. It was found that a constant gain filter can sufficiently provide pointing knowledge, depending on the user's needs. This yaw-estimator also initializes a starting point for a three-axis estimator. Generating a six-state, three-axis control Extended Kalman Filter for attitude determination became the primary focus of research because the FalconSAT 3 payloads require accurate attitude determination. Looking to the future, modification of this three-axis estimator accounts for a pitch wheel considered for FalconSAT 5, a satellite that the U.S. Air Force Academy proposes to launch in December 2009. These three Kalman Filters were developed in England at the Surrey Space Centre and were tested and tuned with magnetometer data of a Surrey Space Technology Limited (SSTL) satellite called UoSAT. Comparing the attitude estimation results to the truth model of UoSAT shows that the Extended Kalman Filters converge to within one degree of accuracy. This paper details the theory behind these Kalman Filters in addition to their tuning and results.

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