Building High Performance Robotic Vision with GMSL

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Figure 1

   

Abstract

Robotic systems increasingly rely on vision to perceive and interact with their environment, creating growing demands for high speed, low latency data links. Gigabit Multimedia Serial Link (GMSL^TM) offers a promising solution by transmitting video, control signals, and power over a single cable with high reliability. This article examines how cameras are deployed in robotics, the connectivity challenges they face, and how GMSL can enable scalable, robust, and performance-driven robotic platforms.

Introduction

Robotic systems increasingly depend on advanced machine vision to perceive, navigate, and interact with their environment. As both the number and resolution of cameras grow, the demand for high speed, low latency links capable of transmitting and aggregating real-time video data has never been greater.

Gigabit Multimedia Serial Link (GMSL™), originally developed for automotive applications, is emerging as a powerful and efficient solution for robotic systems. GMSL transmits high speed video data, bidirectional control signals, and power over a single cable, offering long cable reach, deterministic microsecond-level latency with an extremely low bit error rate (BER). It simplifies the wiring harness and reduces the total solution footprint, ideal for vision-centric robots operating in dynamic and often harsh environments.

The following sections discuss where and how cameras are used in robotics, the data and connectivity challenges these applications face, and how GMSL can help system designers build scalable, reliable, and high performance robotic platforms.

Where Are Cameras Used in Robotics?

Cameras are at the heart of modern robotic perception, enabling machines to understand and respond to their environment in real time. Whether it’s a warehouse robot navigating aisles, a robotic arm sorting packages, or a service robot interacting with people, vision systems are critical for autonomy, automation, and interaction. These cameras are not only diverse in function but also in form—mounted on different parts of the robot depending on the task and tailored to the physical and operational constraints of the platform (see Figure 1).

Autonomy

In autonomous robotics, cameras serve as the eyes of the machine, allowing it to perceive its surroundings, avoid obstacles, and localize itself within an environment. For mobile robots—such as delivery robots, warehouse shuttles, or agricultural rovers—this often involves a combination of wide field-of-view cameras placed at the corners or edges of the robot. These surround-view systems provide 360° awareness, helping the robot navigate complex spaces without collisions.

Other autonomy-related applications use cameras facing downward or upward to read fiducial markers on floors, ceilings, or walls. These markers act as visual signposts, allowing robots to recalibrate their position or trigger specific actions as they move through structured environments like factories or hospitals. In more advanced systems, stereo vision cameras or time of flight (ToF) cameras are placed on the front or sides of the robot to generate three-dimensional maps, estimate distances, and aid in simultaneous localization and mapping (SLAM).

The location of these cameras is often dictated by the robot’s size, mobility, and required field of view. On small sidewalk delivery robots, for example, cameras might be tucked into recessed panels on all four sides. On a drone, they’re typically forward-facing for navigation and downward facing for landing or object tracking.

Automation

In industrial automation, vision systems help robots perform repetitive or precision tasks with speed and consistency. Here, the camera might be mounted on a robotic arm—right next to a gripper or end-effector—and the system can visually inspect, locate, and manipulate objects with high accuracy. This is especially important in pick-and-place operations, where identifying the exact position and orientation of a part or package is essential.

Other times, cameras are fixed above a work area—mounted on a gantry or overhead rail—to monitor items on a conveyor or to scan barcodes. In warehouse environments, mobile robots use forward-facing cameras to detect shelf labels, signage, or QR codes, enabling dynamic task assignments or routing changes.

Some inspection robots, especially those used in infrastructure, utilities, or heavy industry, carry zoom-capable cameras mounted on masts or articulated arms. These allow them to capture high resolution imagery of weld seams, cable trays, or pipe joints—tasks that would be dangerous or time-consuming for humans to perform manually.

Human Interaction

Cameras also play a central role in how robots engage with humans. In collaborative manufacturing, healthcare, or service industries, robots need to understand gestures, recognize faces, and maintain a sense of social presence. Vision systems make this possible.

Humanoid and service robots often have cameras embedded in their head or chest, mimicking the human line of sight to enable natural interaction. These cameras help the robot interpret facial expressions, maintain eye contact, or follow a person’s gaze. Some systems use depth cameras or fisheye lenses to track body movement or detect when a person enters a shared workspace.

In collaborative robot (cobot) scenarios, where humans and machines work side by side, machine vision is used to ensure safety and responsiveness. The robot may watch for approaching limbs or tools, adjusting its behavior to avoid collisions or pause work if someone gets too close.

Even in teleoperated or semi-autonomous systems, machine vision remains key. Front-mounted cameras stream live video to remote operators, enabling real-time control or inspection. Augmented reality overlays can be added to this video feed to assist with tasks like remote diagnosis or training.

Across all these domains, the camera’s placement—whether on a gripper, a gimbal, the base, or the head of the robot—is a design decision tied to the robot’s function, form factor, and environment. As robotic systems grow more capable and autonomous, the role of vision will only deepen, and camera integration will become even more sophisticated and essential.

Robotics Vision Challenges

As vision systems become the backbone of robotic intelligence, opportunity and complexity grow in parallel. High performance cameras unlock powerful capabilities—enabling real-time perception, precise manipulation, and safer human interaction—but they also place growing demands on system architecture. It’s no longer just about moving large volumes of video data quickly. Many of today’s robots must make split-second decisions based on multimodal sensor input, all while operating within tight mechanical envelopes, managing power constraints, avoiding electromagnetic interference (EMI), and maintaining strict functional safety in close proximity to people.

These challenges are compounded by the environments robots face. A warehouse robot may shuttle in and out of freezers, enduring sudden temperature swings and condensation. An agricultural rover may crawl across unpaved fields, absorbing constant vibration and mechanical shock. Service robots in hospitals or public spaces may encounter unfamiliar, visually complex settings, where they must quickly adapt to safely navigate around people and obstacles.

Solve the Challenges with GMSL

GMSL is uniquely positioned to meet the demands of modern robotic systems. The combination of bandwidth, robustness, and integration flexibility makes it well suited for sensor-rich platforms operating in dynamic, mission-critical environments. The following features highlight how GMSL addresses key vision-related challenges in robotics.

High Data Rate and Low Latency

The GMSL2™ and GMSL3™ product families support forward-channel (video path) data rates of 3 Gbps, 6 Gbps, and 12 Gbps, covering a wide range of robotic vision use cases. These flexible link rates allow system designers to optimize for resolution, frame rate, sensor type, and processing requirements (Figure 2).

A 3 Gbps link is sufficient for most surround view cameras using 2 MP to 3 MP rolling shutter sensors at 60 frames per second (FPS). It also supports other common sensing modalities, such as ToF sensors and light detection and ranging (LIDAR) units with point-cloud outputs and radar sensors transmitting detection data or compressed image-like returns.

The 6 Gbps mode is typically used for the robot’s main forwardfacing camera, where higher resolution sensors (usually 8 MP or more) are required for object detection, semantic understanding, or sign recognition. This data rate also supports ToF sensors with raw output, or stereo vision systems that either stream raw output from two image sensors or output processed point cloud stream from an integrated image signal processor (ISP). Many commercially available stereo cameras today rely on this data rate for high frame-rate performance.

At the high end, 12 Gbps links enable support for 12 MP or higher resolution cameras used in specialized robotic applications that demand advanced object classification, scene segmentation, or long-range perception. Interestingly, even some low resolution global shutter sensors require higher speed links to reduce readout time and avoid motion artifacts during fast capture cycles, which is critical in dynamic or high speed environments.

Because GMSL uses frequency-domain duplexing to separate the forward (video and control) and reverse (control) channels, it enables bidirectional communication with low and deterministic latency, without the risk of data collisions. Across all link rates, GMSL maintains impressively low latency: the added delay from the input of a GMSL serializer to the output of a deserializer typically falls in the lower tens of microseconds—negligible for most real-time robotic vision systems. The deterministic reverse-channel latency enables precise hardware triggering from the host to the camera—critical for synchronized image capture across multiple sensors, as well as for time-sensitive, event-driven frame triggering in complex robotic workflows. Achieving this level of timing precision with USB or Ethernet cameras typically requires the addition of a separate hardware trigger line, increasing system complexity and cabling overhead.

Small Footprint and Low Power

One of the key value propositions of GMSL is its ability to reduce cable and connector infrastructure. GMSL itself is a full-duplex link, and most GMSL cameras utilize the power-over-coax (PoC) feature, allowing video data, bidirectional control signals, and power to be transmitted over a single thin coaxial cable. This significantly simplifies wiring, reduces the overall weight and bulk of cable harnesses, and eases mechanical routing in compact or articulated robotic platforms (Figure 3).

In addition, the GMSL serializer is a highly integrated device that combines the video interface (for example, MIPI-CSI) and the GMSL PHY into a single chip. The power consumption of the GMSL serializer, typically around 260 mW in 6 Gbps mode, is favorably low compared to alternative technologies with similar data throughput. All these features will translate to smaller board areas, reduced thermal management requirements (often eliminating the need for bulky heatsinks), and greater overall system efficiency, particularly for battery-powered robots.

Sensor Aggregation and Video Data Routing

GMSL deserializers are available in multiple configurations, supporting one, two, or four input links, allowing flexible sensor aggregation architectures. This enables designers to connect multiple cameras or sensor modules to a single processing unit without additional switching or external muxing, which is especially useful in multicamera robotics systems.

In addition to the multiple inputs, GMSL SERDES also supports advanced features to manage and route data intelligently across the system. These include:

• I²C and GPIO broadcasting for simultaneous sensor configuration and frame synchronization.
• I²C address aliasing to avoid I²C address conflict in passthrough mode.
• Virtual channel reassignment, allowing multiple video streams to be mapped cleanly into the frame buffer inside the systems on chip (SoCs).
• Video stream duplication and virtual channel filtering, enabling selected video data to be delivered to multiple SoCs—for example, to support both automation and interaction pipelines from the same camera feed or to support redundant processing paths for enhanced functional safety.

Safety and Reliability

Originally developed for automotive advanced driver assistance systems (ADAS) applications, GMSL has been field-proven in environments where safety, reliability, and robustness are non-negotiable. Robotic systems, particularly those operating around people or performing mission-critical industrial tasks, can benefit from the same high standards.

Most GMSL serializers and deserializers are qualified to operate across a –40°C to +105°C temperature range, with built-in adaptive equalization that continuously monitors and adjusts transceiver settings in response to environmental changes. This provides system architects with the flexibility to design robots that function reliably in extreme or fluctuating temperature conditions.

In addition, most GMSL devices are ASIL-B compliant and exhibit extremely low BERs. Under compliant link conditions, GMSL2 offers a typical BER of 10–15, while GMSL3, with its mandatory forward error correction (FEC), can reach a BER as low as 10–30. This exceptional data integrity, combined with safety certification, significantly simplifies system-level functional safety integration.

Ultimately, GMSL’s robustness leads to reduced downtime, lower maintenance costs, and greater confidence in long-term system reliability—critical advantages in both industrial and service robotics deployments.

Mature Ecosystem

GMSL benefits from a mature and deployment-ready ecosystem, shaped by years of high volume use in automotive systems and supported by a broad network of global ecosystem partners. This includes a comprehensive portfolio of evaluation and production-ready cameras, compute boards, cables, connectors, and software/driver support—all tested and validated under stringent real-world conditions. For robotics developers, this ecosystem translates to shorter development cycles, simplified integration, and a lower barrier to scale from prototype to production.

GMSL vs. Legacy Robotics Vision Connectivity

In recent years, GMSL has become increasingly accessible beyond the automotive industry, opening new possibilities for high performance robotic systems. As the demands on robotic vision grow with more cameras, higher resolution, tighter synchronization, and harsher environments, traditional interfaces like USB and Ethernet often fall short in terms of bandwidth, latency, and integration complexity. GMSL is now emerging as a preferred upgrade path, offering a robust, scalable, and production-ready solution that is gradually replacing USB and Ethernet in many advanced robotics platforms. Table 1 compares the three technologies across key metrics relevant to robotic vision design.

Conclusion

As robotics moves into increasingly demanding environments and across diverse use cases, vision systems must evolve to support higher sensor counts, greater bandwidth, and deterministic performance. While legacy connectivity solutions will remain important for development and certain deployment scenarios, they introduce trade-offs in latency, synchronization, and system integration that limit scalability. GMSL, with its combination of high data rates, long cable reach, integrated power delivery, and bidirectional deterministic low latency, provides a proven foundation for building scalable robotic vision systems. By adopting GMSL, designers can accelerate the transition from prototype to production, delivering smarter, more reliable robots ready to meet the challenges of a wide range of real-world applications.

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