Design for a space mission
Iris Rover
Design the telemetry data interface for Iris Rover, which will land on the Moon in 2021. (Work in progress)
Iris Rover
Design the telemetry data interface for Iris Rover, which will land on the Moon in 2021. (Work in progress)
Team
Diana Zhan, Shan Wang
Duration
Sep, 2019 - Present
Challenge
When the Iris Rover lands on the Moon and begins its mission, the installed sensors will start to work. These sensors will monitor the health components onboard to make sure the rover functions properly. All data from these sensors will be presented on the telemetry interface through which operators can monitor the rover and troubleshoot when the rover fails.
Solution
We are creating a dashboard to display the latest data from all sensors through which operators can access the overall health of the rover. Meanwhile, detail pages for each sensor type enable operators to further examine data and to tag and document errors if anything goes wrong.
The Process
Discover
Learn from experts
Define
Identify data architecture
Categorize data
Prioritize features
Design
Sketching
Define workflows
Iterate on wireframes
Develop high-fidelity designs
Understand the mission
Learn from stakeholders
This space mission was novel to all of us. When we got the data sheet, we found difficulty in understanding some types of data, let alone knowing how the data could potentially impact the mission. Therefore, to understand the data, we first talked to engineers and operators, and then we conducted a great amount of secondary research. Afterwards, we were more confident in moving forward.
Goals of the telemetry interface
Troubleshooting
Troubleshoot if the rover went wrong through telemetry data.
Monitoring
Monitor the overall health of the rover through the telemetry interface
Define features
Identify data architecture
According to the knowledge we gained from research and interviews, we developed a data architecture that illustrated the flow of information. This structure reinforced our understanding of the data.
Categorize data
Based on the data structure, we categorized data from different types of sensors into this diagram. This diagram showed what data and information needed to be presented on the telemetry interface.
Prioritize features
We further examined the data diagram and came up with a more specific feature list for each data category. We also prioritized the data categories to guide the design in the following steps.
Prioritized data
Temperature
Orientation
Battery
Software/system log
Secondary data
Motor
Communication
Current
Ideate interface
Generate ideas with crazy eight
We initiated our ideation with Crazy Eight practice (coming up with 8 ideas in 8 minutes). After synthesizing of our ideas, we came up with the dashboard design as follows. We applied a module design and arranged the data categories according to the prioritization based on our research.
According to the prioritized feature list, we came up with the design of the dashboard layout. We applied a module design to the dashboard and each module contains a category of data.
Define workflows
According to the design changes and feedback from engineers, we constructed three main workflows related to telemetry data interface: monitoring, error tagging, and error analysis.
Sketch workflows
According to the design changes and feedback from engineers, we constructed three main workflows related to telemetry data interface: monitoring, error tagging, and error analysis.
Key takeways
You never know where the problem will occur. Constant communication with users and stakeholders is the only way we can find flaws and improve the design.
Initiate design with wireframes
Start with sketches
We initiated our ideation with Crazy Eight practice (coming up with 8 ideas in 8 minutes). After synthesizing of our ideas, we came up with the dashboard design as follows. We applied a module design and arranged the data categories according to the prioritization based on our research.
Digitize wireframes
We finished the first draft design of the dashboard on Figma. The purpose of the dashboard was to assist operators in monitoring the overall health of the rover. Therefore, real-time data of sensors with "safe zone" (a data point within the range which would be considered safe) was presented on the dashboard.
Dashboard
The dashboard design
Detail page
The dashboard design
Error analysis
The dashboard design
Key takeaways
In this collaboration, I found our team facing two challenges: how could an innovative team maximize the creativity of each designer, and how could the designers stay consistent in style, while dividing and conquering. A solution to the first problem was picking an appropriate design activity. For example, we used crazy eight to diverge our ideas in the beginning and then converge them at the end. A potential solution to inconsistent styles was using collaborative design tools like Figma.
Encounter challenges
Confront bandwidth limitation
When we presented our design to the engineering team, the feedback caught us off guard. They had just confirmed that the bandwidth CuberRover used for data transmission was limited. Therefore, the CubeRover wouldn’t be able to send entire data points collected wouldn’t simultaneously, which meant that there wouldn’t be "real-time data”. In addition, the long distance transmission caused a 4s delay in data. Operators also wanted to mark and document errors manually for troubleshooting.
Key takeaways
Due to the bandwidth limitation and other feedback, we made design changes as follows:
Enable data resolution adjustment to offset the limitation of bandwidth.
Add manual error tagging feature.
Add an error analysis page.
Add the angular acceleration data into the dashboard.
Detail page
Dashboard
Dot graph
Move on with challenges
According to the feedback from the operator and engineers, we updated our design as follows.
Dashboard changes
Changed all line graphs to dot graphs.
Rearranged layout based on prioritization.
Detail page
Error analysis
Create high-fi designs
According to the discussion with front-end engineers and the rover operators, we came up with the final design of the detail pages. Big changes included:
A master timeline enabling operators to zoom and move all data charts simultaneously for inspection.
Error marks in data charts allow operators to enter error analysis pages for troubleshooting.
Dashboard
A master timeline enabling operators to zoom and move all data charts simultaneously for inspection.
Detail pages
A master timeline enabling operators to zoom and move all data charts simultaneously for inspection.
Error analysis
A master timeline enabling operators to zoom and move all data charts simultaneously for inspection.
After we developed high-fi designs, we met with the rover operator to validate our design.
Collect feedback from the rover operator
Next step:
In the next two months, we are finishing the hi-fi design dashboard, detail pages, and error tagging pages. In the end, we will create spec sheets to help front-end engineers implement the design.