GMSL and the growing ecosystem around robotic vision systems

Just a few years ago, many site owners were satisfied if a robot could move from point A to point B. That’s not quite enough anymore. Today’s robots are being asked to move faster, operate in more dynamic environments, and deal with more obstacles along the way. As those demands increase, vision systems are becoming indispensable for navigation and spatial awareness.

“The biggest challenge is no longer just the image quality itself,” says Stephen Liu, robotics lead at embedded systems developer Advantech. “It’s system-level orchestration. As sensor counts grow, robotic OEMs have to manage bandwidth, latency, synchronization, and compute all at the same time.”

These systems move large amounts of data in real time, and if interfaces cannot sustain throughput, perception becomes unstable. Sensor fusion also depends on precise timing; even a few milliseconds of drift between cameras, lidars, and IMUs can degrade navigation accuracy.

“Robots don’t just see—they have to decide and act instantly,” says Liu. “It requires a lot of coordination between the GPU, MPUs, and real-time operating system to deliver this deterministic performance.”

In harsh environments, the demands become even harder to manage. Robots may have to maintain performance amid vibration, dust, water, and extreme temperatures, while also routing cables through compact designs.

“As cable length increases, connectors are stressed, and ESD interference becomes much more of a concern,” explains Liu. “We require very stable synchronized vision input and long-distance vision transmission, especially for ruggedized situations.”

One technology being applied across the robotics sector to support these vision architectures is GMSL.

“GMSL is a game changer for multi-camera robotics,” says Liu. “You can carry high-resolution video, control signals, and synchronization over a single lightweight cable, reliably and with very low latency. That dramatically reduces cabling complexity, improves EMI resistance, and supports precise hardware-level time synchronization. From an integration perspective, it can also simplify system design.”

Similar architectures have been used in automotive systems for years. As the GMSL ecosystem has matured, the design approaches have moved into robotics.

“This transition is very natural,” explains Liu. “Automotive systems like ADAS and autonomous driving already solved many of the same problems robotics faces today, like multiple synchronized cameras, long cable runs, harsh operating conditions. Robots operating in warehouses, farms, or cities are in fact like vehicles themselves. They move fast, operate for long hours, can’t tolerate perception failures. So by bringing automotive-grade GMSL technologies into robotics, teams get proven robustness, deterministic latency, and scalability.”