Visualize the impact of atmospheric opacity on rover instruments to help scientists reduce mission planning time.
To enhance mission efficiency, the The Mars 2020 team at NASA Jet Propulsion Laboratory (NASA JPL) is working to reduce rover daily planning time from 8 to 5 hours. Rover planning at JPL is a complex decision-making process, requiring consensus among various scientific and engineering teams to maximize discovery while maintaining the safety of the rover.
In 6 months, our team worked with NASA JPL to research and design L-RROI, a web-based platform that visualizes atmospheric opacity (tau) to help scientists quickly understand its impact on instruments and improve the decision-making process.
- Collaborators: Yomna Hawas, John Sykes, Angela Yung
- User interviews, competitive assessment, research synthesis, design requirements, storyboard.
- Apr - Aug, '19
- 6 months
Long-term Routing and Rover Instrumentation (L-RROI) is a web-based platform that uses the predictability of tau to help the mission teams shift from short-long to long-term planning, increasing efficiency for the Mars 2020 mission.
Anticipate and Validate
The current process of obtaining and analyzing tau data is lengthy and inefficient. L-RROI processes daily tau data from orbiters and landers and reconstructs it on top of the Martian globe. At a quick glance, scientists can gain situational awareness of dust behavior and plan ahead for a global dust storm. They can also switch between Mars missions: Mars 2020, Curiosity, and Insight.
Explore Interactive Interface
Scientists prefers the ability to customize and explore options. L-RROI accommodates this by making the tool fully interactive. Scientists can click on the rover path to explore tau value at a certain time. Besides, the tool was built with robust hover states and tooltips to enhance usability and learnability.
Make Informed Decision
During the planning process, scientists can select the instruments they are interested in using. L-RROI takes the input, integrates tau data, visualizes instrument conditions, and helps scientists identify the best time to conduct an activity. This help scientists quickly eliminate time frames that could potential affect rover's safety, enhancing collaboration among the science and engineering teams.
See past data
Understanding the pattern of historical data is critical for the mission teams to evaluate and assess current rover plans. Scientists can use L-RROI to compare and contrast data, seeing the pattern overtime in order to plan, study, and predict future dust behaviors.
Compare and Contrast
Dust increases camera exposure time and could harm rover instruments. Knowing what the photos would look like at a certain time in the mission can help scientists anticipate and allocate proper resources. L-RROI helps scientists achieve that with the "Rover View" feature. Scientists can drop a pin on the map to view past or predicted image. They can further compare and contrast with other images to gain a holistic view of tau effects.
Understand the problem
Atmospheric opacity (tau) is a measure of optical depth or how much sunlight can penetrate the atmosphere. Since Mars is covered with dust, the higher the tau value means there’s more dust in the air, and less sunlight can reach the surface.
We started off by conducting literature reviews to understand the impact of atmospheric opacity (tau) on past missions and on Mars 2020. We further looked into other topics such as NASA JPL structure, weather variables, collaboration, and data visualization to develop a holistic understanding of the problem space.
- Similar to the effect of water on Earth, dust dominates the surface of Mars and dictates most of its weather conditions.
- Tau has both direct and indirect impacts on rover instruments and data.
User & Expert Interviews
We selected 18 JPL scientists, engineers, and data visualization experts as the research audience. Semi-structured interviews, directed storytelling, and iterative diagraming were the 3 main methods for our research activity.
Meeting the proposed 5-hour operational timeline is unattainable unless rover teams shift focus to long-term goals.
In the very beginning, it took about 12 hours to just send one day's worth of uplink to Curiosity. I think it reduced down to an average of like seven to eight. On really great days, [...], they got really close to five.
Weather no longer poses a critical risk to rover safety, but still must be considered due to its impact on instruments and power constraints.
[Engineering] is going to get touchy about pointing the cameras very high up elevation when there is a lot of dust in the atmosphere. Atmospheric observations may be curtailed because you don't want to get your camera stuck pointing at the sky with a bunch of dust falling out of it.
Weather on Mars is relatively predictable, however, there is no weather forecasting despite its potential applications to long-term planning purposes.
Scientists [predicted]...that Mars was going to get a really big dust storm and it did. That's why Opportunity no longer works, but they were able to predict it fairly well and that's because there's this pattern pretty frequently that occurs on Mars.
Unavailable atmospheric opacity (tau) measurements in internal tools negatively impact operational efficiency.
Before [scientists] go off and start harassing the folks that do the image processing to get it into the pipeline of what's wrong and why is this broken? It's a lot nicer to go, ‘oh, okay, I'm dealing with the local dust storm, kick the tau up for a day or two.’
Despite tactical disagreements and the varying cultures, rover safety takes precedence because without a rover, there is no scientific discovery.
Engineers, of course, are extremely cautious..[..] we don't want to stand on the science, because, of course, that's how we get our next round of funding. [...] it's a balance, [...] in order to make sure that we're doing the best we can with the science collection, while also keeping the rover safe.
In the conflict between mission groups, the only shared language is data. Even then, every specialty has its own dialect, leading to misunderstandings.
The geologist has his own kind of knowledge that comes from his expertise. The atmospheric scientists would have a different set of knowledge. Sometimes that's helpful, but also sometimes comes in the way of them seeing the data from the other person's perspective. So, how do you help them maybe step into each other's shoes?
Data is more revealing when contextualized with other observations. Existing tools do not have this capability, hindering scientific discovery.
For meteorology, time of day is crucial. So if there were a way to compare and contrast a couple of different days, that would be really helpful. Instead of looking at the left half of this plot and the right half. You'd have to visually try to compare this feature to that feature, and that would be awkward.
The use of numerous custom-built tools is “both a feature and a bug”; it makes output inconsistent, but leads to advancement through productive scientific discussions.
For a given set of recorded data, there are definitely people giving different interpretations or different explanations, but I think that’s how the science commands you in general.
Craft design requirements
The advancement of scientific knowledge stands to benefit from disagreement as dissent breeds productive debate in order to reconcile conflicts. Our tool needs to facilitate healthy discussion between differing points of view in order to drive scientific advancement.
Prioritize customization and flexibility
Scientific discoveries stem from finding novel ways to analyze and interpret data. To promote further exploration, the tool needs to provide scientists the ability to explore a dataset in various ways without disrupting existing work preferences.
See through the same lens
Scientists are using their own tools and this could lead to misunderstandings about the meaning of their output. As a result, enabling shared mental models is crucial in order to ground conversations.
Show how things fit in a bigger picture
Scientists and engineers should understand how their work impacts each other and the overall mission. This can be done through contextualizing data to provide a comprehensive view of the situation.
Understanding the history of data is just as important as understanding what it means. To scientists, knowing the provenance of data facilitates trust, exposes caveats, and helps ground the conversations.
Perform competitive analysis
We approached the competitive analysis with the intention to assess a variety of tools related to collaboration, communication, mapping, and data visualization. The analysis included translational products to better understand how other industries approach solving issues related to collaboration in time-pressing situations.
Ideate and determine use cases
We are currently in the process of building and testing the prototype. More details will come soon :)
If you're also a space nerd like I am, here's a live video of the Mars 2020 rover being built and tested in the Spacecraft Assembly Facility at NASA JPL.