Matrix evaluation of science objectives

The most fundamental objective of all robotic planetary spacecraft is to return science data. To accomplish this, a spacecraft is fabricated and built, software is planned and coded, and a ground system is designed and implemented. However, the quantitative analysis required to determine how the collection of science data drives ground system capabilities has received very little attention. This paper defines a process by which science objectives can be quantitatively evaluated. By applying it to the Cassini Mission to Saturn, this paper further illustrates the power of this technique. The results show which science objectives drive specific ground system capabilities. In addition, this process can assist system engineers and scientists in the selection of the science payload during pre-project mission planning; ground system designers during ground system development and implementation; and operations personnel during mission operations

Science planning and sequencing for Cassini

This paper will address the science planning and sequencing aspects of the command generation process for the scientifically diverse Cassini Mission. The mission's prime objectives are to study the Saturnian system and deliver the Huygens Probe to the moon Titan. Together, the spacecraft and probe will be the largest and most complicated craft ever launched to another planet. The presentation will begin with an overview of the Cassini spacecraft and its scientific instrumentation. This will be followed with a description of the Oct. 1997 mission. Next, the structure of the science planning and sequencing process, with special emphasis on science's role, will be outlined. Finally, this presentation will conclude with a discussion of some of the unique challenges faced by the Ground System during Cassini's four-year orbital tour

The PI Launchpad: Expanding the base of potential Principal Investigators across space sciences

The PI Launchpad attempts to provide an entry level explanation of the process of space mission development for new Principal Investigators (PIs). In particular, PI launchpad has a focus on building teams, making partnerships, and science concept maturity for a space mission concept, not necessarily technical or engineering practices. Here we briefly summarize the goals of the PI Launchpad workshops and present some results from the workshops held in 2019 and 2021. The workshop attempts to describe the current process of space mission development (i.e. space-based telescopes and instrument platforms, planetary missions of all types, etc.), covering a wide range of topics that a new PI may need to successfully develop a team and write a proposal. It is not designed to replace real experience but to provide an easily accessible resource for potential PIs who seek to learn more about what it takes to submit a space mission proposal, and what the first steps to take can be. The PI Launchpad was created in response to the high barrier to entry for early career or any scientist who is unfamiliar with mission design. These barriers have been outlined in several recent papers and reports, and are called out in recent space science Decadal reports.Comment: 7 Pages, 2 Figure, Accepted to Frontier

Market-based systems for solving space exploration resource allocation problems

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN061386 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A Market-Based Mechanism for Allocating Space Shuttle Secondary Payload Priority

This is an investigation into the design of a market-based process to replace NASA's current committee process for allocating Shuttle secondary payload resources (lockers, Watts and crew). The market-based process allocates budgets of tokens to NASA internal organizations that in turn use the budget to bid for priority for their middeck payloads. The scheduling algorithm selects payloads by priority class and maximizes the number of tokens bid to determine a manifest. The results of a number of controlled experiments show that such a system tends to allocate resources more efficiently by guiding participants to make resource and payload tradeoffs. Most participants were able to improve their position over NASA's current ranking system. Furthermore, those that are better off make large improvements while the few that do worse have relatively small losses. Copyright Kluwer Academic Publishers 2000mechanism design, auctions, scheduling,