In December, NASA took another small, incremental step towards autonomous surface rovers. In a demonstration, the Perseverance team used AI to generate the rover’s waypoints. Perseverance used the AI waypoints on two separate days, traveling a total of 456 meters without human control.
“This demonstration shows how far our capabilities have advanced and broadens how we will explore other worlds,” said NASA Administrator Jared Isaacman. “Autonomous technologies like this can help missions to operate more efficiently, respond to challenging terrain, and increase science return as distance from Earth grows. It’s a strong example of teams applying new technology carefully and responsibly in real operations.”
Mars is a long way away, and there’s about a 25-minute delay for a round trip signal between Earth and Mars. That means that one way or another, rovers are on their own for short periods of time.
The delay shapes the route-planning process. Rover drivers here on Earth examine images and elevation data and program a series of waypoints, which usually don’t exceed 100 meters apart. The driving plan is sent to NASA’s Deep Space Network (DSN), which transmits it to one of several orbiters, which then relay it to Perseverance. (Perseverance can receive direct comms from the DSN as a back up, but the data rate is slower.)
AI Enhances Mars Rover Navigation
In this demonstration, the AI model analyzed orbital images from the Mars Reconnaissance Orbiter’s HiRISE camera, as well as digital elevation models. The AI, which is based on Anthropic’s Claude AI, identified hazards like sand traps, boulder fields, bedrock, and rocky outcrops. Then it generated a path defined by a series of waypoints that avoids the hazards. From there, Perseverance’s auto-navigation system took over. It has more autonomy than its predecessors and can process images and driving plans while in motion.
There was another important step before these waypoints were transmitted to Perseverance. NASA’s Jet Propulsion Laboratory has a “twin” for Perseverance called the “Vehicle System Test Bed” (VSTB) in JPL’s Mars Yard. It’s an engineering model that the team can work with here on Earth to solve problems, or for situations like this. These engineering versions are common on Mars missions, and JPL has one for Curiosity, too.
“The fundamental elements of generative AI are showing a lot of promise in streamlining the pillars of autonomous navigation for off-planet driving: perception (seeing the rocks and ripples), localization (knowing where we are), and planning and control (deciding and executing the safest path),” said Vandi Verma, a space roboticist at JPL and a member of the Perseverance engineering team. “We are moving towards a day where generative AI and other smart tools will help our surface rovers handle kilometer-scale drives while minimizing operator workload, and flag interesting surface features for our science team by scouring huge volumes of rover images.”
AI’s Expanding Role in Space Exploration
AI is rapidly becoming ubiquitous in our lives, showing up in places that don’t necessarily have a strong use case for it. But this isn’t NASA hopping on the AI bandwagon. They’ve been developing automatic navigation systems for a while, out of necessity. In fact, Perseverance’s primary means of driving is its self-driving autonomous navigation system.
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