Modern warehouse automation increasingly relies on autonomous mobile robots to move goods with speed, precision, and intelligence. To support real time navigation, sensor fusion, and fleet level communication, a robotics developer required a powerful computing solution capable of operating reliably within mobile robotic systems. A modular GPU-capable industrial edge AI computer delivered the processing performance, flexible I/O expansion, and industrial durability needed to power next generation AMR deployments in smart warehouse environments.
Challenges
- Navigation and sensor coordination workloads exceeded the limits of the previous CPU and memory configuration
- The robotic system needed to connect LiDAR, cameras, CAN controllers, and safety modules without external adapters
- High-resolution perception sensors increased bandwidth demands, making 10GbE expansion necessary
- Vision processing workloads required support for discrete GPU acceleration
- Continuous vibration and warehouse deployment standards required a rugged UL-certified system
Solution
- Premio’s super-rugged modular AI edge inference computer (RCO-6000-RPL)
- Intel Core processors (Series 2) with hybrid architecture for navigation and sensor workloads
- Rich onboard industrial I/O including USB, serial, CAN, and isolated digital I/O
- EDGEBoost I/O expansion supporting optional 10GbE networking for high-bandwidth sensors
- EDGEBoost Node supporting RTX PRO 4000 SFF Blackwell GPU acceleration
- Rugged enclosure with wide temperature support, MIL-STD-810H alignment, and UL certification
Benefits
- Local Engineering Support in the United States
Company Overview
A robotics technology company focused on warehouse automation develops autonomous mobile robots that help streamline material movement inside modern fulfillment centers. Its solutions combine intelligent navigation, real-time data processing, and fleet management software to improve efficiency across warehouse operations. By continuing to advance robotics and automation technologies, the company supports the growing need for faster and more intelligent logistics systems.
The Challenges
Higher CPU and Memory Requirements for Navigation Processing
Autonomous mobile robots process localization data, motion control inputs, and sensor streams continuously as they move through warehouse environments. As more sensors and software functions were added, the previous system began to struggle with responsiveness. A higher-performance processor with additional memory capacity was needed to maintain stable real-time operation.
Expanding Connectivity and Bandwidth for Robotic Subsystems
Reliable communication between LiDAR sensors, cameras, CAN controllers, and safety modules was essential to the robotic platform. However, limited native interfaces required additional adapters, increasing integration complexity and taking up valuable internal space. In addition to sufficient onboard I/O, the system also needed higher network bandwidth to handle large data streams from high-resolution LiDAR and multi-camera sensors. Support for expanded industrial I/O and 10GbE connectivity helped streamline subsystem integration while maintaining stable real-time perception performance.
Support for Discrete GPU Acceleration
Vision-based workloads such as object recognition and obstacle detection benefit from GPU acceleration at the edge. However, integrating a GPU inside a mobile robot requires a platform that can support expansion without increasing system size or thermal load. The robotics developer needed a solution that could add accelerator support within the existing chassis design.
Rugged Reliability for Warehouse Deployment Environments
Warehouse robots operate in constant motion and are regularly exposed to vibration during daily use. The onboard computer needed to stay stable under these conditions over long operating cycles. UL certification also helped ensure the system met safety requirements for deployment in warehouse facilities.
The Solution
Premio’s super-rugged modular AI edge inference computer (RCO-6000-RPL)
After assessment, the customer chose the RCO-6000-RPL as the onboard computing platform for its robotic system. The solution combines Intel Core processor (Series 2) performance with DDR5 memory and the industrial R680E chipset to support navigation control and subsystem coordination at the edge. Its modular architecture, especially support for EDGEBoost I/O and EDGEBoost Nodes, provided flexible expansion for 10GbE networking, GPU acceleration, and future system upgrades within the same deployment architecture.
Hybrid Core Architecture for Real Time Robotic Processing
Intel Core processors (Series 2) use a hybrid core architecture that allows navigation and sensor processing tasks to run on performance cores while background services run on efficiency cores at the same time. This helps keep robotic control workloads responsive during operation. Support for up to 96GB of DDR5 SODIMM memory also allows the system to handle multiple sensor data streams more reliably at the edge.
Comprehensive Industrial Connectivity for Robotic Subsystems
The RCO-6000-RPL provides extensive onboard connectivity including USB 3.2 Gen 2, RS-232/422/485, CAN, dual 2.5GbE LAN, and isolated digital I/O. These interfaces allowed direct connection to sensors, controllers, and communication modules without additional adapters. This simplified integration across the robotic subsystems and reduced overall system complexity.
EDGEBoost I/O Expansion for High-Speed Networking and Accelerators
EDGEBoost I/O supports expansion modules such as 10GbE networking for high-bandwidth perception sensors. This allows large data streams from LiDAR and multi-camera systems to be transferred faster and with lower latency during operation. As a result, the robotics developer could maintain more consistent real-time sensor processing without changing the base system configuration.
Rugged Mechanical Design for Continuous Mobility Deployments
EDGEBoost Node enables support for the RTX PRO 4000 SFF Blackwell GPU to accelerate vision-based processing directly on the robot. The GPU provides additional parallel compute performance for workloads such as object detection, mapping, and obstacle recognition while maintaining a small deployment footprint suitable for mobile systems. Adaptive smart fan control inside the chassis automatically adjusts airflow based on thermal conditions, helping stabilize GPU temperatures and maintain consistent performance during changing workloads.
Industrial Certification for Deployment Readiness
The RCO-6000-RPL met UL requirements for deployment in warehouse automation environments where safety compliance is expected. It is also designed in alignment with MIL-STD-810H to handle vibration during continuous robot operation. This helped ensure the system remained reliable during long-term use in mobile robotics applications.
The Benefit
Local Engineering Support in the United States
With headquarters located in Los Angeles, Premio provides responsive engineering and technical support within the United States. This proximity allows customers to receive faster assistance during development, integration, and deployment. Access to local engineering expertise helps system integrators accelerate project timelines and resolve technical challenges more efficiently.
Conclusion
As warehouse automation continues to advance, reliable edge computing plays an important role in supporting intelligent robotic operations. With its modular design, flexible EDGEBoost expansion, and strong industrial connectivity, the RCO-6000-RPL supports autonomous mobile robots operating in modern warehouse environments. For more information about the RCO-6000-RPL or other rugged edge computing solutions, please contact sales@premioinc.com.