Morbilidad diagnosticada y sentida

4.5.1

Utility of robotic precursor mission

Exploratory, robotic reconnaissance controlled by a mission control has the potential to significantly improve scientific return from lunar surface exploration. In particular, data from robotic precursor missions can be used to narrow scientific focus (i.e., develop

specific research questions and hypotheses to test), improve traverse planning, reduce operational risk, and increase crew productivity.

We found that the primary scientific value of an exploratory reconnaissance mission is providing surface geology visualization at resolutions and from viewpoints not

achievable from orbit. High-resolution surface imagery of surrounding areas on the scale of 10’s of meters up to several km in extent was most useful. The most used data sets included large scale panoramic images that allowed a full contextual view of the surrounding area including exposure of rocks and traversability of the area and lidar scans that provided range and scale information.

From this experience, it is suggested that the reconnaissance mobility can be more reduced then the mobility needed later for crew transport (i.e., crew rover). A ‘small’ rover with the ability to collect panoramic photography and lidar scans would meet baseline needs. This type of rover would be required to access low lying areas (e.g., floor of impact crater) to view side of steep topography and reach high points to get panoramic views of the region.

4.5.2

New human exploration paradigm

During the Apollo EVAs, planned timelines were carefully followed by teams of mission controllers and science support personnel in the back rooms on Earth. Rarely were the crews "allowed" to get far behind this timeline (Eppler et al., 2013). New geological operation strategies for the Moon should be more flexible and allow the ability to change EVA schedules in real time, whether the final authority in making changes lie with the lunar crew or Mission Control.

On Earth, time spent at a specific locality can range from brief noting of position and the geology, to a multi-hour session involving considerable sampling, note-taking, and observation. A more flexible exploration approach can take advantage of the level of crewmember capabilities and adapt to the complexity of the geology observed at a particular site (Mader et al., 2012). In addition, future lunar missions will have an advantage over Apollo missions in that higher resolution images from orbital spacecraft

will be available and the astronauts will have access to these images in the field using display units. This will allow field crews to have access to the same background information as the ground support back on Earth, which would further enhance their ability to make decisions in the field.

In addition to better data sets and viewing capabilities in the field, future lunar missions will likely involve real-time data return. Other analogue missions that have tested real- time data return have had difficulty in analyzing data in a time to affect the next day’s traverse plan (Eppler et al., 2013; Yingst et al., 2014). The ILSR phased-approach accounts for limited timelines of EVA’s by maximizing the time spent making

observations and ensuring that expertise of mission control scientists can help inform the planning of subsequent traverses. The focus of the reconnaissance traverse is to get a regional understanding of the geology and choose specific sites to study in detail during the follow-on detailed traverses.

By focusing on specific hypotheses to test, the astronauts and mission control were better able to prioritize geological tasks in a short four-day mission. However, a real challenge of planetary missions is answering a complex set of related and disparate problems with limited resources. In future work it would be useful to test the ILSR phased approach while addressing multiple hypotheses addressing different geological questions.

4.5.3

Data management

Simulating planetary missions on Earth can help test data management procedures, in order to help identify where current needs in data management architectures exist. Future surface lunar missions will operate on much faster time scales than the Mars Exploration Rover missions or other deep space missions. In addition, more data will be generated during a human mission than a rover mission over the same time scale. Operators and scientists will be required to maintain real-time situational awareness, quickly assimilate data from a rover or astronaut crew, and be able to plan or re-plan activities in response to incoming information (Deans et al., 2012).

The process of documentation and the roles of humans, machines, and automation in the process, will have a large impact on science return and the optimization of surface activities. Crew time required for traverse planning, data analysis, and other pre/post EVA activities will vary with mission duration and level of crew autonomy. Keeping track of incoming data products and their modification during a fast-paced exploration campaign is not trivial.

The UWO analogue mission program did not construct or use a specific software

program for managing science data (e.g., NASA xGDS; Deans et al., 2012). Much of our data management was done manually. A customized system could automate many key processes, such as file naming and storage, aid in recording data history, and enable effective searching of raw and modified data. Key recommendations for designing and/or adding to existing systems include:

• Allow multiple ways of searching data, for example use “Tags” that would allow data to be queried by: instrument, date and time, site (e.g., site name, station number), location (e.g., enter coordinates or draw a point or area on map), who modified it, type of modifications, and if data (e.g. image) was used in

instructions to the field team;

• Link raw and modified versions of the same data;

• Develop an indicator signal that informs mission control that data has arrived from the field;

• Automate file naming, archiving, and sorting of data into appropriate file structure;

• Create conventions addressing which software programs will be used to modify data (e.g., Photoshop, Adobe Illustrator) and ensure that file formats being used are compatible across applications.