Avnet and Weston Robot have jointly developed an autonomous inspection platform that combines quadruped robots with edge AI computing to automate factory inspections.
The system runs AI workloads directly on the device using AMD Ryzen AI Embedded processors, delivering 50 TOPS of performance and enabling low-latency decision-making even without cloud connectivity.
By moving inspection data processing to the edge, organizations can detect problems faster and reduce costs compared to manual inspection methods.
What happened
Tech distributor Avnet and robotics integrator Weston Robot have created an autonomous inspection platform powered by quadruped robots equipped with Avnet's computing backpack. The system runs on AMD Ryzen AI Embedded processors and delivers up to 50 TOPS of AI performance, enabling onboard AI inference, thermal and visual analytics, 3D lidar mapping, and operation in GPS-denied environments.
Why it matters
The platform processes inspection data at the edge rather than in the cloud, enabling low-latency decision-making and operation even where cloud connectivity is limited. Organizations can detect operational issues earlier, reduce inspection costs, and improve operational continuity by automating what are currently manual, resource-intensive inspection processes.
What to watch
When paired with Weston's fleet management software, the robot can patrol and inspect a defined facility path, stopping to investigate inspection sites using onboard cameras and sensors. New inspection tasks can be trained and deployed directly to the robot, enabling capabilities beyond the standard Unitree quadruped robot.
Avnet, a supplier of industrial compute architecture, and Weston Robot, a Singapore-based robotics integrator, have jointly developed an autonomous inspection platform designed to automate routine factory monitoring tasks. The system is built around a quadruped robot equipped with a backpack containing Avnet's computing components.
Weston Robot integrates quadruped, humanoid, and mobile robot-based applications for customers, with use cases spanning inspection, security, and facility management. The company primarily resells Unitree robotic solutions and also manufactures custom autonomous mobile robot (AMR) platforms and unmanned maritime systems; it typically delivers these through a robots-as-a-service (RaaS) model. Avnet's contribution is the on-device computing architecture that turns the base quadruped into an intelligent system. The Avnet architecture delivers up to 50 TOPS of AI performance and is powered by AMD Ryzen AI Embedded processors. It supports AI-driven inspection, thermal and visual analytics, 3D lidar mapping, and reliable operation in GPS-denied environments—settings where satellite navigation and cloud connectivity may be unavailable.
The key technical advantage is edge processing: the platform executes AI workloads directly on the robot rather than sending data to the cloud. This enables low-latency AI inference, faster decision-making, and operation in environments with limited or no cloud connectivity. When combined with Weston's fleet management software, the robot can be assigned a mission to patrol and inspect a defined path through a facility. Using onboard cameras and sensors, the robot stops to investigate inspection sites along its patrol route. Critically, new inspection and recognition tasks can be trained and deployed directly to the onboard computing architecture, enabling the robot to adapt to new inspection or perception requirements without hardware modification—capabilities not available with the standard Unitree quadruped alone.
According to Arthur Chung, Vice President of Sales & Supplier Management at Avnet Asia, the collaboration brings "together advanced computing, edge AI and robotics to help organizations scale industrial intelligence, strengthen safety and improve operational resilience." Dr. Zhang Yanliang, Chief Scientist at Weston Robot, emphasized that the platform addresses a widespread operational challenge: "Industrial environments generate enormous volumes of operational data, yet many inspection processes remain manual, reactive and resource-intensive." By automating inspection with intelligent autonomous systems, organizations can continuously monitor critical infrastructure, detect anomalies earlier, and deliver actionable insights in real time.
The partnership represents a convergence of three technical trends: robotics hardware (quadrupeds), edge AI computing (onboard inference), and factory automation. Avnet's role is to supply the computing architecture that transforms a standard quadruped robot into an autonomous inspection system capable of running AI models locally. By processing data at the edge—on the device itself rather than in a remote cloud—the platform overcomes a critical operational challenge: factories often lack reliable or fast cloud connectivity, and sending video streams to the cloud introduces latency that delays decision-making. The combination of Avnet's 50 TOPS compute capacity with Weston Robot's fleet management software and domain expertise creates a system flexible enough to handle multiple inspection tasks. The ability to train and deploy new inspection models directly to the robot without hardware changes is what distinguishes this from deploying a fixed quadruped; it shifts inspection from a pre-programmed task to an adaptive one.
For industrial organizations, the business case rests on automation of manual work. Today, facility inspections rely on human operators moving through environments to spot problems—a slow, reactive process. By deploying robots that continuously patrol and investigate, organizations can detect anomalies earlier, reduce the labor cost of routine inspection, and improve safety by having machines enter hazardous environments. The platform's support for thermal imaging, 3D lidar mapping, and operation in GPS-denied spaces (underground facilities, buildings with poor satellite signal) broadens the range of industrial settings where deployment is practical.
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