Casio is deploying visible light communication technology, originally designed for 1990s video games, to guide lunar exploration robots for the Japan Aerospace Exploration Agency (JAXA). This revival of legacy hardware, alongside similar shifts at IBM, Microsoft, and Sony, marks a growing industrial trend where previously sidelined technologies are finding new utility in the era of AI and autonomous systems.
Casio’s Lunar Navigation Breakthrough
Casio’s collaboration with JAXA utilizes a specialized light-based tracking system to navigate lunar craters. According to the Nikkei, this technology allows for precise three-dimensional mapping in shadowed lunar terrain where standard radio waves often fail. The system originated in the mid-1990s as a motion-tracking tool for baseball console games. It was shelved at the time due to the limitations of existing camera sensors, which lacked the performance required for reliable commercial deployment.
Did you know?
Casio’s light communication system was originally intended to detect human swinging motions in sports video games before it was adapted for space exploration decades later.
IBM Mainframes and the AI Infrastructure Shift
The IBM mainframe, a staple of corporate computing in the 1970s and 1980s, has shifted from a perceived legacy burden to a central component of modern AI architecture. While the rise of cloud computing once signaled the decline of the mainframe, demand for internal, high-performance data processing has reversed this trend. Integrating AI directly into mainframes is increasingly viewed as more efficient and secure than migrating sensitive institutional data—such as financial and government records—to external cloud environments.
Repurposing Consumer Hardware for Robotics
Technology discontinued in the consumer market is frequently finding a second life in industrial automation. Microsoft’s Kinect, a 3D motion-sensing peripheral launched for the Xbox in 2010, provides a clear example. Although Microsoft discontinued the device due to a lack of compatible gaming content, its depth-sensing capabilities are now used in logistics. According to industry reports, these sensors enable autonomous robots to avoid obstacles and assist in automated cargo loading within warehouses.
Sony’s Evolution of the Image Sensor
Sony has repositioned its image sensor business to address the stagnation of the global smartphone market. By developing “intelligent sensors” that integrate AI processing directly onto the chip, Sony has moved beyond simple image capture. These sensors now autonomously analyze data, a capability essential for the development of autonomous vehicles, smart factories, and humanoid robots. This shift has allowed Sony to secure new growth in sectors that require real-time, on-device data processing.
Strategic Reinterpretation of Legacy Tech
A tech industry source noted that the success of these transitions often depends on the maturity of surrounding ecosystems. A historical precedent is the QR code, which Denso developed in the 1990s for automotive inventory. It remained a niche industrial tool until the widespread adoption of smartphones turned it into a global standard for payments and authentication. The ability to reinterpret these past technologies within the context of current AI and robotics environments is emerging as a critical skill for modern technology firms.
Pro Tip:
When evaluating legacy systems, focus on the core data processing or sensing capability rather than the original consumer application. Often, the underlying physics of a 30-year-old sensor remains highly relevant even if the original product interface is obsolete.
Frequently Asked Questions
Why are old technologies being used in space exploration?
According to reports on the JAXA project, legacy technologies like Casio’s visible light communication can solve specific environmental challenges—such as navigating shadowed lunar craters—where modern radio-based systems struggle to maintain signal integrity.
Why is the IBM mainframe still relevant?
Mainframes are being repurposed as high-performance AI servers. They allow large institutions to process data internally, which avoids the significant security risks and costs associated with migrating sensitive data to the cloud.
How does AI change the value of hardware sensors?
AI allows sensors to move beyond raw data collection. By integrating AI processing, sensors can now autonomously select and analyze necessary information, turning simple hardware into intelligent edge-computing devices used in autonomous vehicles and robotics.
Are you seeing legacy technology being used in unexpected ways in your industry? Share your observations in the comments below or subscribe to our newsletter for more deep dives into industrial tech trends.
Keep reading