Getting to the Moon was the first chapter. Interlune and Astrolab are working on how to operate there.
Updated
March 6, 2026 1:32 AM
Apollo 17 Astronaut's Snapshot of Taurus-Littrow Valley. PHOTO: UNSPLASH
As plans for a long-term human presence on the Moon pick up pace, the focus is shifting from landing there to working there. It is one thing to reach the surface. It is another to build roads, prepare sites and extract materials in a way that can support real activity.
That is where Interlune and Astrolab come in. Interlune is a space resources company. Astrolab builds planetary rovers. The two are now working together to mount Interlune’s lunar digging system onto Astrolab’s Flexible Logistics and Exploration (FLEX) rover. They have completed a concept study and are planning hardware testing in Houston.
The aim is straightforward: combine a rover that can move reliably across the Moon with equipment that can dig, collect and handle lunar soil. Interlune is focused on harvesting natural resources from the Moon, starting with helium-3. To do that at scale, the system cannot sit in one place. It has to move across the surface, handle dust and operate in harsh conditions. "Reliable, autonomous mobility is crucial to the Interlune harvesting system and broader lunar infrastructure development", said Rob Meyerson, co-founder and CEO of Interlune. "Astrolab's FLEX is the right vehicle for the job".
By fitting its digging and collection hardware onto FLEX, Interlune is working toward a mobile system that can gather large amounts of lunar soil and support future construction needs. Beyond helium-3, the same setup could help prepare base sites, level ground, build protective barriers and lay the groundwork for other structures. In simple terms, it is about turning a rover into a working machine for the Moon.
The partnership also connects to Interlune’s work with Vermeer Corporation to develop equipment for continuous, high-volume digging adapted to lunar conditions. Taken together, the goal is to build systems that can support both commercial and government missions — whether that means resource extraction or preparing land for future bases.
For Astrolab, the collaboration strengthens the role of FLEX as more than just a transport vehicle.
"Working with Interlune further differentiates FLEX as the rover of choice for commercial and government Moon missions", said Jaret Matthews, Astrolab founder and CEO. "Interlune's expertise in developing and testing highly specialized regolith simulant will further enhance FLEX's ability to mitigate dust and operate in extreme environments".
Testing will be centered in Houston, which is becoming an important hub for commercial space development. Astrolab was the first company to lease space at the Texas A&M University Space Institute, currently under construction at NASA’s Johnson Space Center. Interlune operates the Houston-based Interlune Research Lab, where it creates and tests simulated versions of lunar soil.
That detail matters. Moon dust is fine, abrasive and difficult to manage. Before any hardware flies, it needs to prove it can survive and function in those conditions. By testing their systems in realistic soil simulants, the companies can refine how the rover moves and how the digging system performs.
The Houston lab is partially funded by the Texas Space Commission, reflecting the growing role of regional space initiatives in supporting private companies building beyond Earth. Overall, the collaboration is not about grand promises. It is about integrating hardware, running real tests and taking practical steps toward operating on the Moon.
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Where smarter storage meets smarter logistics.
Updated
January 8, 2026 6:32 PM
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Kioxia's flagship building at Yokohama Technology Campus. PHOTO: KIOXIA
E-commerce keeps growing and with it, the number of products moving through warehouses every day. Items vary more than ever — different shapes, seasonal packaging, limited editions and constantly updated designs. At the same time, many logistics centers are dealing with labour shortages and rising pressure to automate.
But today’s image-recognition AI isn’t built for this level of change. Most systems rely on deep-learning models that need to be adjusted or retrained whenever new products appear. Every update — whether it’s a new item or a packaging change — adds extra time, energy use and operational cost. And for warehouses handling huge product catalogs, these retraining cycles can slow everything down.
KIOXIA, a company known for its memory and storage technologies, is working on a different approach. In a new collaboration with Tsubakimoto Chain and EAGLYS, the team has developed an AI-based image recognition system that is designed to adapt more easily as product lines grow and shift. The idea is to help logistics sites automatically identify items moving through their workflows without constantly reworking the core AI model.
At the center of the system is KIOXIA’s AiSAQ software paired with its Memory-Centric AI technology. Instead of retraining the model each time new products appear, the system stores new product data — images, labels and feature information — directly in high-capacity storage. This allows warehouses to add new items quickly without altering the original AI model.
Because storing more data can lead to longer search times, the system also indexes the stored product information and transfers the index into SSD storage. This makes it easier for the AI to retrieve relevant features fast, using a Retrieval-Augmented Generation–style method adapted for image recognition.
The collaboration will be showcased at the 2025 International Robot Exhibition in Tokyo. Visitors will see the system classify items in real time as they move along a conveyor, drawing on stored product features to identify them instantly. The demonstration aims to illustrate how logistics sites can handle continuously changing inventories with greater accuracy and reduced friction.
Overall, as logistics networks become increasingly busy and product lines evolve faster than ever, this memory-driven approach provides a practical way to keep automation adaptable and less fragile.