
_Space situational awareness simulation with satellites and space debris modelled and rendered using a.i. solutions’ FreeFlyer software._

As a software engineering lead at [a.i. solutions](https://ai-solutions.com/), Stefan Novak has helped clients like [NASA](https://www.nasa.gov/) and the [Air Force](https://www.airforce.com/) plan hundreds of successful excursions into outer space. His team in particular focuses on [FreeFlyer](https://ai-solutions.com/freeflyer-astrodynamic-software/), a software application that provides support for space mission design, analysis, and operations. Its customizable interfaces have a lot in common with Foxglove Studio – making it no surprise that Stefan used Studio earlier this year to build custom visualizations for upcoming space missions.

We had the pleasure of sitting down with Stefan to discuss the projects he’s working on, and how Foxglove Studio has helped his team plan for the next satellite launch.

**How would you describe your role and work at a.i. solutions?**

I work on a small team at a.i. solutions that develops [FreeFlyer](https://ai-solutions.com/freeflyer-astrodynamic-software/), our commercial space mission design software. FreeFlyer has been used on 225+ space missions, supporting everything from early mission design to mission operations. At its core, it is a high-fidelity orbital simulation engine with a powerful IDE built around it. It’s used by researchers and startups alike, with organizations like NASA, the Air Force, and [NOAA](https://www.noaa.gov/) using it to perform the complex analysis required for space mission operations.

For some context on what our work entails, my first project on the team was implementing [terrain modeling](https://www.youtube.com/watch?v=EB4P0wTic4w) to compute high-fidelity line-of-sight calculations. This lets us determine when regions of terrain are visible or not visible from a spacecraft, particularly when a spacecraft sets below the horizon in a mountainous environment.

One of my favorite moments at work is watching a satellite that used FreeFlyer to plan its trajectory and compute its maneuvers finally launch! There are a lot of interesting computer science problems to work on, while also being able to contribute to future space missions.

**How did you first become interested in robotics?**

![first robot](/images/blog/spotlight-stefan-novak-of-ai-solutions/first-robot.png)
_L: My first robot, built using a radio-controlled toy chassis. R: An interactive Flash site I created to demonstrate how the control system worked._

During a senior design project for my undergraduate degree in mechanical engineering, my team designed and built an operational self-driving car that would follow another radio-controlled car using optical navigation. I developed the steering and engine control systems and implemented the software system. My favorite part of the project was creating an interactive 2D simulation of the steering algorithm in Macromedia Flash!

I was also exposed to robotics working as a Test Officer for the U.S. Army, my first job out of college. I got to ride around in the back of M3A3 Bradley tanks while testing on-board software. I even got to test a self-driving Humvee. During that time, I was able to attend the [DARPA Urban Challenge in 2007](https://www.youtube.com/watch?v=aHYRtOvSx-M) and see a variety of self-driving car platforms in-person.

**What drew you to leveraging robotics for space missions?**

![globe](/images/blog/spotlight-stefan-novak-of-ai-solutions/globe.bmp)
_Hypothetical large-scale satellite constellation and coverage map for the continental US, modeled and rendered using FreeFlyer._

There is no shortage of challenging engineering problems in the space domain – your work can contribute to advancements in earth science, space weather, global communications networks, and even exploration of the solar system. Satellites generate a lot of telemetry data which is then processed and visualized in ways similar to modern robotics. I’ve found that the Robot Operating System (ROS) is a great fit for handling the “data plumbing” of telemetry within a satelllite ground system or mission operations environment. It's exciting to take open source technology that was built for the ground, and use it for space. I think there’s a lot of potential in leveraging open source robotics software for supporting space missions!

**What does a typical day for you look like? What projects are you working on now?**

Currently, I'm developing tools that can be used for space missions where a satellite will be approaching and docking with an older satellite that needs to be refueled and serviced. The new satellite would have optical, lidar, and infrared sensors to detect the older satellite and estimate its pose (i.e. its position and orientation).

![space mission](/images/blog/spotlight-stefan-novak-of-ai-solutions/space-mission.png)
_Simulation of an approach and rendezvous with the International Space Station, modeled and rendered using FreeFlyer._

While on the project, I realized that this is similar to problems that self-driving car companies have to solve. I started researching the tools used in that domain, which is how I came across ROS and Foxglove Studio. While FreeFlyer is useful for simulating telemetry data, it is less geared towards managing that data. Foxglove Studio, on the other hand, is designed to integrate with ROS, making it easy to manage telemetry data and leverage built-in message types that map directly to the problem I was working on. Its UI is also expressly designed around easily choosing a telemetry topic to investigate, visualizing it with rich 3D scenes, and debugging it with interactive controls.

**How did Foxglove Studio help achieve your goals?**

We wanted to overlay a wireframe of a satellite’s CAD model on top of an optical image taken by another satellite. The wireframe needed to be positioned and aligned on top of the image using the satellite’s estimated pose. If there was an error in the on-board pose estimate, we want to see that visually - the wireframe wouldn’t be aligned with the satellite in the image underneath. This would give the satellite operators a warning that the on-board pose estimation algorithm wasn’t converging on the right solution, and the operators would be able to send a command up to the satellite to reseed its pose estimation algorithm.

To do this, I developed a [custom Foxglove panel](https://gist.github.com/stefan-pdx/473d11763e1ff526287d8dd01e322890) that renders camera images and pose telemetry data together in a [Babylon.js](https://www.babylonjs.com/) scene, with the image as the background and the wireframe in the foreground. I implemented this data visualization using the [Foxglove Studio Extensions Manager](https://github.com/foxglove/fox), a developer-friendly command-line tool for creating new extensions and building, packaging, and installing them.

![custom extension](/images/blog/spotlight-stefan-novak-of-ai-solutions/extension.gif)
_Foxglove Studio dashboard displaying images and telemetry simulated by FreeFlyer._

I found the Foxglove Extensions API very well designed. It makes it easy to publish and subscribe to ROS messages within your custom panel, and to use those incoming messages to update React UI components. I was able to render data from multiple ROS topics, and bundle up additional Javascript dependencies into my extension (in my case, Babylon.js). You can also embed any dynamic HTML or Javascript into a Foxglove panel pretty easily! It’s really cool that Foxglove provides the tooling to do this and that I was able to build this custom visualization without having to fork Foxglove.

**What do you have planned next?**

I’ve just submitted an abstract to speak at the [Ground System Architectures Workshop](https://gsaw.org/), an annual gathering to talk about software architectures for satellite mission control centers. In it, I argue that satellite ground systems have a lot in common with autonomous vehicles. Just like self-driving cars, modern spacecraft generate large amounts of live telemetry data, use sensors to locate targets, and continuously update their models of the world.

If my abstract is accepted, I’ll be sharing the lessons I learned from leveraging free and open source tools used in the self-driving car domain (like ROS and Foxglove Studio!) for satellite rendezvous and proximity operations (RPO). It will be interesting to see how the same tools that catalyzed such exciting progress in autonomous vehicle development could be just as relevant to the space community.

Check out the talk I presented at GSAW last year here: [_Cloud-based Processing of Flight Dynamics Data Pipelines_](https://vimeo.com/511777503).

**What advice would you share with anyone interested in getting into space missions?**

There are a lot of different avenues to be a part of the space industry, depending on your passions! Within the space industry, there are opportunities for people with backgrounds in anything – from robotics and hardware design, to modeling and simulation, to software and quality testing. It’s also a pretty small and tight-knit industry, and I love connecting people to opportunities that are on my radar.

![](/images/blog/spotlight-stefan-novak-of-ai-solutions/shuttle.jpg)
_The Atlantis Space Shuttle, on display here at the Kennedy Space Center near Cape Canaveral, FL, has inspired awe and wonder in generations of onlookers._

If you’re interested in learning more, you can follow my updates on [LinkedIn](https://www.linkedin.com/in/stefan-novak/) and [Instagram](https://www.instagram.com/nasapdx/). I love learning what types of problems people enjoy working on, and helping them translate that to opportunities in the space industry, so please feel free to connect with me!



If you’re familiar with Foxglove’s [history](/about), you may know that many members of our team previously worked at self-driving car company [Cruise](http://getcruise.com/), and that Foxglove Studio began as a fork of Cruise’s [Webviz](https://github.com/cruise-automation/webviz) project.

Many people are curious about how these two apps compare. While Foxglove Studio and Webviz can appear similar at first glance, their capabilities have diverged significantly.

So let’s get into it – where do these applications overlap, and where do they differ?

# Similarities

**Web-based**

Both Webviz and Foxglove Studio are **available as web applications** – which means getting started with visualizing your data can be as easy as opening a browser window and navigating to a URL. They’re both **built on cutting-edge web technologies** like [WebGL](https://get.webgl.org/) and [WebAssembly](https://webassembly.org/) to render their rich and interactive visualizations.

**Open source**

Both Webviz and Foxglove Studio are **available to the wider robotics community at no cost**. While Foxglove does offer paid plans for hosted services (i.e. cloud-synced team layouts, remote data storage, etc.), both applications welcome contributions and feedback from any open source user. By being open source, both projects allow any hobbyist, researcher, or engineer to try out the tool before committing to it.

**Integrated development environment**

Both Webviz and Foxglove Studio ease the workflow pains that commonly plague robotics development by **integrating the tools for visualizing and debugging robotics data into a single environment**. Instead of pulling up multiple tools in separate windows (i.e. [rviz](http://wiki.ros.org/rviz), [rqt](https://wiki.ros.org/rqt), etc.) to explore your data, you can open one workspace to tackle a variety of workflows.

In both applications, you can choose from a suite of modular [panels](/docs/panels/introduction) – that do everything from plot values of interest, render interactive 3D scenes, and drill down into topic messages – to compose tiled [layouts](/docs/layouts). You can then rearrange and configure these panels before sharing the final layout with your teammates. This avoids context switching and keeps you in one seamless development environment, helping you focus on iterating on your robots.

![panels](/images/blog/foxglove-studio-vs-webviz/panels.png)
_Foxglove Studio and Webviz both offer a suite of configurable panels._

# Differences

While our goals and development philosophies are similar, Webviz and Foxglove’s features have diverged significantly. Though Webviz is an open source project, Cruise has understandably focused their development on autonomous driving use cases, and the open source components are tightly coupled with proprietary extensions. As a result, there are many outstanding bug reports and feature requests from the community.

While Foxglove Studio drew inspiration from Webviz, we are taking it in a new direction. We are a true community project that collaborates closely with open source contributors – from our colleagues over at [Open Robotics](https://www.openrobotics.org/), to [professional robotics engineers](/blog/spotlight-ryan-govostes-software-engineer-at-WHOI) and [hobbyists](/blog/spotlight-aditya-kamath-robotics-systems-engineer-on-akros).

![a.i. solutions](/images/blog/foxglove-studio-vs-webviz/ai-solutions.png)
_a.i. solutions has used Foxglove Studio to visualize data rendered and simulated by their space mission design software [FreeFlyer](https://ai-solutions.com/freeflyer-astrodynamic-software/)._

Whether you work in agriculture, [aerospace](/blog/spotlight-stefan-novak-of-ai-solutions), or [ocean exploration](/blog/spotlight-ryan-govostes-software-engineer-at-WHOI), we want to build features that help your team achieve more.

Check out the new features we’ve shipped and workflows we’ve unlocked.

## New panels

Foxglove Studio adds several panels that we learned would be valuable to robotics teams, regardless of industry:

- [Map panel](/docs/panels/map) – Display [sensor_msgs/NavSatFix](https://docs.ros.org/en/api/sensor_msgs/html/msg/NavSatFix.html) messages as points on a map.
- [Parameters panel](/docs/panels/parameters) – Read and set rosparams from your connected ROS source.
- [Topic Graph panel](/docs/panels/topic-graph) – Display a computational graph of ROS nodes and topics in your data source.
- [URDF Viewer panel](/docs/panels/urdf-viewer) – Use a [Unified Robot Description Format](http://wiki.ros.org/urdf) (URDF) file to visualize your robot model.

Because Foxglove Studio is so modular and flexible, this list of available panels will always be growing! If you have an idea for a general purpose panel that you’d like to add to Studio, open a [GitHub issue](https://github.com/foxglove/studio/issues).

## New data sources

While Foxglove Studio and Webviz both support ROS 1 Rosbridge connections and bag files, Foxglove Studio supports several additional [data connection options](/docs/connection/data-sources):

- [ROS 2 connections](/blog/announcing-live-ros2-support) (Rosbridge and native)
- [ROS 2 bag playback](/blog/announcing-ros2-bag-support)
- [Native ROS 1 connections](/docs/connection/native)
- [Velodyne LiDAR connections](/docs/connection/velodyne)

![velodyne lidar](/images/blog/foxglove-studio-vs-webviz/velodyne.png)
_Studio can now connect directly to Velodyne LiDAR hardware to visualize the velodyne scan._

We want to continue expanding our supported data sources! Open a [GitHub issue](https://github.com/foxglove/studio/issues) to share any data formats that you'd like to see Foxglove Studio support for.

## Cross-platform web and desktop app

We made it easier to get started by making Foxglove Studio available as both a web and desktop app. Having worked with ROS, we didn’t like that installing native tools required a very specific environment. But no matter your setup, you can get up and running with Foxglove Studio using your browser or our desktop app for Linux, macOS, and Windows.

**Our web app is great for people who want to try Foxglove Studio before downloading the desktop app, or who need a low-friction way to quickly view data from their robots**. The barrier to entry is eliminated – you can simply open a browser window and navigate to [studio.foxglove.dev](https://studio.foxglove.dev) to start exploring your data.

**The desktop app unlocks features like our native ROS connections and [custom panel extensions](/blog/announcing-foxglove-studio-extensions)**. It allows you to connect directly to TCP sockets and access your local filesystem, which have led to features like native ROS connections and [custom panel extensions](/docs/extensions/getting-started).

![web and desktop apps](/images/blog/foxglove-studio-vs-webviz/apps.png)
_Foxglove Studio is available as both a web (Chrome) and desktop app (Linux, Windows, macOS)._

## Layout management

Foxglove Studio adds support for **creating, editing, and sharing multiple personal layouts**. We also announced more collaboration support with our latest release of [team layouts](/blog/announcing-foxglove-for-teams). In the same way you can create a Google Drive of shared docs that you can edit and collaborate on with your team, you can now create Studio teams to share popular layouts with.

Since roboticists have different workflows to accomplish, we want to provide all the tools needed to build and configure the workspaces to tackle these different tasks.

## Custom panel extensions

While we want to provide general purpose robotics tools across specific applications, we know that robotics teams will always have project-specific needs that we may not always be able to address out-of-the-box. In response to this gap, we [released custom extensions](/blog/announcing-foxglove-studio-extensions) to **empower you to develop your own bespoke panels**. Instead of waiting on new features, you can now add custom functionality to your local instance of Foxglove Studio directly.

![extensions](/images/blog/foxglove-studio-vs-webviz/extensions.png)
_Foxglove Studio allows you to write custom panels and load them into your local environment._

We hope to support sharing extensions among your team, similar to layouts for teams. We are always expanding on our [extensions API](/docs/extensions/panel-api-methods) – if you have any particular requests, please [let us know](/contact)!

## Data platform

Most recently, we released [Foxglove Data Platform](/blog/manage-your-robotics-data-with-foxglove-data-platform) – a scalable new service for **managing your organization’s robotics data**. It functions as a **companion product to Foxglove Studio**, and is closely integrated with its visualization and debugging capabilities. Using the convenient web interface, you can locate events of interest, then stream them directly in Foxglove Studio for instant visualization and further analysis.

![data platform](/images/blog/foxglove-studio-vs-webviz/data-platform.png)
_Foxglove Data Platform provides one central data repository that allows you to upload, explore, and stream your robotics data._

Foxglove Data Platform currently supports ingesting ROS 1 or ROS 2 data, but we are just getting started. [Schedule a demo](/demo) to learn more, or [contact us directly](/contact) to let us know what you’d like to see in the future.

# Stay in touch

While we initially drew inspiration from Webviz to accelerate robotics development, we want to expand on what we learned to help Foxglove Studio evolve with the industry.

We’re excited to find other widely useful features to implement – most immediately on the horizon, we plan to **expand our extensions API, support more data sources, and flesh out our Foxglove Data Platform capabilities**. We’re also continuing to talk with industry decision makers, participate in working groups, and brainstorm new ways we can streamline robotics development for everyone.

Regardless of the scale, type, and purpose of your robots, we want to help you spend less time fighting your tools, and more time iterating on your robots. [Download the app](/download) and [check out our docs](/docs) to see how Studio can accelerate your robotics development.

If you have any questions or feedback, you can join our [Slack community](/join-slack), or find us on [GitHub](https://github.com/foxglove/studio) and [Twitter](https://twitter.com/foxglovedev).



Robotics data should be easy to find, explore, and understand. Historical data is critical to informing future development, and managing this data effectively will help you iterate faster on your robots.

Earlier this month, we announced [team layouts](https://foxglove.dev/blog/announcing-foxglove-for-teams) – our first collaboration feature for Foxglove. **Today we’re excited to announce [Foxglove Data Platform](/data-platform), a scalable new service for managing your organization’s robotics data.** With our world-class infrastructure that can scale from gigabytes to petabytes, Foxglove Data Platform is your one-stop shop for storing, analyzing, and visualizing your data.

# Reimagining data management

Data management can be time-consuming and expensive, taking away valuable bandwidth and focus from your engineering team.

The sheer volume of real-world and simulated data that robotics developers must sift through is a non-trivial hurdle on its own. Team members are forced to manually upload and download huge files, or pass hard drives around the office to share data with one another. As a team grows, so does its accumulated and duplicated data, making it increasingly difficult to know what information lives where and how to access it quickly.

Foxglove Data Platform tackles each of these hurdles, allowing you to store and explore data more efficiently. Our convenient web interface and API let you upload data to a central team repository, and our strategic data partitioning and indexing allows you to retrieve the information you need at lightning speed.

Exploring your data has also become more intuitive. You can locate events of interest by querying for a specific robot, a particular time range, or for given topics. For further analysis, you can stream it directly in Foxglove Studio for instant visualization and debugging.

# We 💜 feedback

[Foxglove Data Platform](/data-platform) is just getting started – we’re excited to hear your thoughts on what we have so far, and what you’d like to see in the future.

We currently support ingesting robotics data in ROS 1 or ROS 2 formats, and can integrate with other frameworks easily. We offer either a fully managed service to get you up and running quickly, or a self-hosted alternative that uses your private VPC or on-premises data center.

Check out our [pricing](/pricing) or [book a demo](/demo) to get started with Foxglove Data Platform.


Manage your robotics data with Foxglove Data Platform

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Manage your team settings and data through a convenient web UI.

Explore and analyze meaningful segments of your data.

Use our public API endpoints or CLI to quickly retrieve the data that you're interested in.

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