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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The Cassini Spacecraft will soon journey to Saturn to perform a close-up study of the Saturnian system; its rings, moons, magneto-sphere, andf the planet itelf. Sequences of commands will be sent to the spacecraft by ground personnel to control every aspect of the mission. To validate and verify these command sequences, a bit-level, high-speed simulator (HSS) has been developed.
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Our work focuses upon development of techniques for choosing among a set of alternatives in the presence of incomplete information and varying costs of acquiring information.
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An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains resource margins.
The Mars Exploration Rovers Mars '03 mission is one of NASA's most ambitious science missions to date. The rovers will be launched in the summer of 2003 with each rover carrying instruments to conduct remote and in-situ observation to elucidate the planet's past climate, water activity, and habitability. Science is the primary driver of MER and, as a consequence, making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this critically, the MER project has selected MAPGEN (Mixed-Initiative Activity Plan GENerator) as an activity planning tool. MAPGEN combines two exiting systems, each with a strong heritage: APGEN the Activity Planning tool from the Jet Propulsion Laboratory and the Europs Planning/Scheduling system from NASA Ames Research Center. This paper discusses the issues arising from combining these tools in the context of this mission.
This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from high-level goals using AI-based planning software.
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In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
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The purpose of this paper is to report the results of a one-year project aimed at demonstrating the concept of Just-In-Time (JIT) Planning.
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