AGVs, AMRs, and automated forklifts are transforming the logistics and materials handling industry. As distribution centers are facing higher demand with faster shipping times, automation is no longer a forward-looking option, but an operational necessity. These mobile robotic platforms are reshaping how goods are picked, sorted, and transported to significantly improve warehouse productivity.
At the core of this transformation lies not just robotics, but intelligent edge computing. The ability to process real-time data from perception sensors, execute complex AI models, and operate reliably in dynamic warehouse environments is crucial for scalable autonomy.
This case study explores how a leading robotics and technology company successfully retrofitted Premio’s rugged RCO-6000-RPL Series into their AMR and AGV platforms to bring their AI-powered mobility solutions to market. The partnership represents a leap from prototype to production, powered by edge computing designed for the harsh realities of industrial automation.
Company Background
In response to rising e-commerce demands, increasing throughput pressures, and labor shortages, this robotics company has engineered a flexible lineup of mobile robotic solutions. With a portfolio of autonomous forklifts, tote retrieval systems, and hybrid robotic platforms, their mission is to automate repetitive and labor-intensive tasks in the supply chain by deploying fleets of autonomous mobile robots.
While their software-defined robotics architecture was proven in early-stage simulations and trials, scaling the solution for commercial deployment demanded a hardware foundation that could support real-time edge inferencing, versatile connectivity, and mission-critical reliability.
The Challenges
Transitioning from a software prototype to a production-ready robotic system came with a handful of technical complexities. The company’s initial proof-of-concept was based on consumer-grade hardware, sufficient for lab testing but unsuitable for operational environments where reliability and uptime are critical. Their engineering team faced the following core challenges:
Real-Time AI Inferencing Requirements
For fully autonomous operation, robotic systems are needed to continuously process and interpret real-world data using advanced AI models. These included deep learning algorithms for visual perception, such as convolutional neural networks (CNNs) to detect pallets, obstacles, human workers, and lane markings in real-time. While traditional cloud computing infrastructures could be utilized to process these intensive workloads, it is unfeasible in a mission-critical, latency-sensitive deployment.
While the robotic company’s initial proof-of-concept utilized high-performance GPUs, it lacked the rugged thermal design required for sustained inferencing cycles in enclosed, mobile platforms. Moreover, the short lifecycle and limited support of consumer hardware posed risks to scalability and long-term sustainability.
High-Density Connectivity Demands
The autonomous robotics were designed with up to eight PoE (Power-Over-Ethernet) vision cameras to provide 360-degree situational awareness to enable object detection, environmental mapping, and so on. Connecting these many vision cameras presented a significant challenge since multiple sensors are needed to simultaneously connect and deliver the necessary video data. Off-the-shelf systems typically lack the support for the required number of PoE ports and, in some cases, could not support PoE altogether. This challenge of consolidating and supporting multiple PoE cameras posed quite a hurdle.
Industrial and Mobile Deployment Difficulties
Operating inside fast-paced distribution centers and expansive warehouses introduced environmental stresses beyond what traditional computing systems are built to handle. AGVs and AMRs experience continuous vibrations and directional movement and are expected to withstand rigorous warehouse environments. The robotics company needed an edge computer that could not only be capable of operating in these harsh conditions but also be implemented into the autonomous robot itself.
The Solution
After an in-depth evaluation of the company’s functional requirements and deployment constraints, Premio recommended the RCO-6000-RPL Series, our flagship AI Edge Inferencing Computer purpose-built to process complex inferencing workloads in rugged edge deployments. It has already proven its capabilities in similar applications, including autonomous B2B delivery vehicles and automated forklift solutions. Let us explore how its modularity, high-performance processing capabilities, and industrial reliability made it an ideal fit for the robotics company’s transition from prototyping to full-scale production.
Real-Time Processing Power
To support autonomous operation without reliance on continuous cloud connectivity, real-time data processing is needed directly onboard the robotic platform. The RCO-6000-RPL is configured with a 13th Gen Intel® Core™ processor, featuring a performance hybrid architecture allowing for optimized resource allocation and task execution. This hybrid core design introduces Performance (P) cores for prioritized intensive workloads and Efficiency (E) cores for multitasking management. As for RAM, it leverages 64GB of DDR5 memory, doubling the throughput compared to its predecessor to buffer multi-channel video streams and volumes of sensor data simultaneously.
Edge AI Acceleration Leveraging EDGEBoost Nodes
To support advanced AI inferencing, the RCO-6000-RPL leveraged an EDGEBoost Node to integrate an NVIDIA® RTX™ 4000 Ada GPU, offering 20GB of GDDR6 memory and up to 327.6 TFLOPS of tensor performance. This professional-grade accelerator enabled the execution of deep neural networks for visual recognition, object detection, and spatial mapping in real time. With a 130W TDP and a slim single-slot form factor, the RTX 4000 Ada was optimized for confined spaces typical in autonomous robotics, delivering high compute density without compromising thermal performance or energy efficiency.
Scalable Connectivity with EDGEBoost I/O Technology
To accommodate up to eight PoE vision cameras used for situational awareness, obstacle detection, and environmental mapping, Premio outfitted the system with dual EDGEBoost I/O (EBIO) modules. This allowed the robotics platform to support synchronized, high-bandwidth video input while providing power directly through the Ethernet connections. The modular EBIO design reduced integration complexity and gave the company the flexibility to customize I/O configurations across different robotic vehicles without the need for OEM-level redesigns.
Built-In Vehicle Communication and Wireless Connectivity
The RCO-6000-RPL featured a built-in CAN Bus interface for standardized communication with sensors, motor controllers, and actuators, enabling seamless integration with the mobile robotic systems. It also supported wireless modules for Wi-Fi, LTE, or 5G connectivity, allowing the robots to transmit telemetry data, and interface with hybrid cloud networks. This dual support for wired and wireless communication ensured robust data flow between each autonomous unit and the central management infrastructure.
Built Rugged. Built Ready.
Designed for rugged industrial environments, the RCO-6000-RPL features a fanless and cableless design tested to MIL-STD-810G standards for shock and vibration resistance and operated reliably across a wide temperature range from -25°C to 70°C. Its 9–48VDC power input supported unstable power environments typical in mobile robotics, with protections for overvoltage, reverse polarity, and power surges. These ruggedization features ensured that the system could operate continuously in high-mobility scenarios, minimizing downtime and maximizing performance in warehouse and logistics environments.
The Benefits
By working directly with Premio as their edge hardware partner, the robotics and technology company gained a long-term solution that enabled faster deployment, reduced engineering overhead, and improved system reliability.
Embedded Product Longevity
Premio’s industrial computers are designed with long product lifecycles, ensuring hardware consistency over extended deployment periods. This allowed the robotics company to standardize across its product line without worrying about mid-cycle obsolescence or requalification efforts.
Compliance and Deployment Confidence
Premio’s systems are built to meet NDAA and TAA compliance requirements and are UL listed, providing peace of mind during deployment in regulated or safety-sensitive environments. These certifications accelerated time-to-market by reducing the need for additional testing or component sourcing delays.
Trusted Engineering Partnership
Beyond the hardware, the company benefited from Premio’s engineering expertise, agile supply chain, and deep experience in rugged edge deployments. Premio’s ability to support custom I/O configurations and pre-validated GPU solutions helped reduce time spent on integration and testing.