Welcome to Vision Vitals, e-con Systems' insightful podcast on everything embedded vision-related.
In today's episode, we shift from lab concepts to the floor of real deployment — where Autonomous Mobile Robots face the toughest conditions.
Many AMR projects look promising during design but struggle once they meet factory dust, uneven lighting, or network interference. The gap between simulation and reality is where vision plays its most demanding role.
To find out how camera systems help bridge that gap, we're joined by an expert from e-con Systems.
Good to have you here.
Glad to be part of it. Real-world deployment is where engineering decisions are tested. Cameras that look perfect on paper can behave differently when exposed to heat, vibration, or glare. e-con Systems works with customers across industries to solve those practical challenges before full-scale rollout.
Let's start there. What are the most common issues AMRs face during deployment?
Speaker:
The first challenge is integration. AMRs have multiple subsystems such as navigation, control, and perception — all running simultaneously. If camera synchronization is even slightly off, the robot may misread its position or miss obstacles.
Then there's lighting. Warehouses and factories have mixed illumination from skylights, LEDs, and reflections off metal surfaces. Without proper HDR tuning, frames lose contrast and algorithms struggle.
Durability also matters. Cameras must resist vibration, dust, and temperature variation without losing focus. Finally, data flow must stay stable. When high-resolution streams overload the bus or processor, latency builds up and reaction time drops.
Host:
How does e-con Systems address the pain points related to the technical setup?
Speaker:
We look at it as a system-wide problem, not just a component issue. Cameras are selected, tuned, and validated together with the compute platform.
Using calibrated ISPs such as TintE, we optimize color balance, exposure, and frame timing before data reaches the perception model. We also work with interfaces like GMSL2 that maintain high-speed transmission across long cables, avoiding signal loss in electrically noisy spaces.
And because most AMRs run on NVIDIA Jetson modules, our cameras come with ready drivers and synchronization support. That shortens setup time and ensures predictable performance when the robot goes live.
Host:
Can you give a few real-world examples from deployments?
Speaker:
Sure. Warehouse automation is one of the busiest areas. Robots move heavy loads through aisles filled with dynamic activity. Cameras mounted on the vehicle feed data to navigation software, helping detect humans, forklifts, and misplaced goods.
Some customers use global shutter cameras for motion-heavy operations, while others rely on HDR sensors to handle changing lighting between storage zones and open docks. Depth cameras like Time-of-Flight units measure distance to shelves, improving docking accuracy.
Host:
Where else are AMRs being used effectively besides warehouses?
Speaker:
A growing number of deployments are happening in security and facility management. Patrol robots equipped with RGB and NIR cameras monitor restricted zones, detect movement, and send live footage to control centers.
In chemical plants, camera-equipped AMRs check for gas leaks and heat anomalies through thermal sensors paired with RGB feeds. At airports, similar systems handle crowd observation and unattended baggage detection.
Host:
That covers surveillance and safety, right? What about communication and interaction?
Speaker:
Telepresence is a great example. Remote employees can log into an AMR platform to navigate through office floors or classrooms. High-quality cameras ensure smooth streaming with correct exposure and color balance, even when the robot moves through uneven lighting.
In education, that same concept lets students participate in labs or lectures without being physically present. Vision accuracy keeps the experience natural — the robot responds in real time to facial and positional cues.
Host:
How are cameras helping in scenarios where service and customer-facing roles are involved?
Speaker:
Hotels, restaurants, and office complexes now deploy service robots for delivery and assistance. Cameras detect people approaching, adjust movement to avoid collision, and identify visual markers for navigation.
Wide-angle sensors provide peripheral awareness, while depth cameras measure distances in crowded lobbies. Our clients also use RGB-IR modules for low-light areas such as hallways or evening events.
By tuning exposure and frame rate for the specific environment, we make sure the robot maintains reliable perception without external light adjustment.
Host:
As you may know, every deployment has its own constraints. How does e-con Systems adapt camera systems for that diversity?
Speaker:
Customization is key. We design solutions based on payload, field of view, and processing budget. Some projects need compact USB modules, others demand rugged GMSL systems.
We also evaluate software integration early. Ensuring ROS compatibility and pre-validating image tuning on target processors means that customers can save significant engineering time.
And since many deployments involve multiple cameras, we test synchronization under real-world vibration and EMI conditions to confirm consistent frame capture. That makes the system production-ready instead of just prototype-ready.
Host:
What are the biggest lessons teams learn after their first large-scale rollout?
Speaker:
They learn that consistency matters more than raw specification. It's easy to pick a high-resolution camera, but if the exposure or synchronization drifts, the perception model loses reliability.
They also see the value of long-term durability testing. Camera mounts, connectors, and cable routing must withstand months of continuous operation. Once you fix those elements early, the overall stability of the AMR improves dramatically.
Host:
As AMRs continue to expand across industries, what future trends do you see shaping camera design?
Speaker:
Cameras are becoming smarter. Image pre-processing is moving closer to the sensor, reducing load on the main processor. Compact multi-camera units with factory-calibrated synchronization will become common.
We're also seeing more interest in edge analytics, where basic object detection happens directly on the camera before data reaches the CPU. That means faster decisions, lower bandwidth, and higher efficiency for fleet operations.
Finally, hybrid imaging, using RGB with depth or IR, will continue to grow because it provides richer environmental context under all lighting conditions.
Host:
That's a solid view of how vision shapes deployment success.
Before we wrap up, any message for teams planning their first AMR rollout?
Speaker:
Start with the environment, not the spec sheet. Understand lighting, obstacles, and required reaction time before choosing the sensor. Partnering with a camera specialist early can save months of testing and redesign.
e-con Systems helps customers take that path with ready-to-integrate cameras tuned for performance, durability, and platform compatibility.
Host:
That's a great note to end on.
We've seen how real-world AMR deployments demand more than theory — they need cameras that hold up under vibration, lighting change, and data stress.
To know more about e-con Systems' camera solutions for AMR environments, visit www.e-consystems.com.
Thanks for listening to Vision Vitals. Keep exploring, keep learning, and join us next time for another look into the future of intelligent mobility.
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