UAB Rubedo Sistemos, a robotics solutions developer, secured a €135,236 R&D grant with ProBaltic Consulting to develop an AI-based smart robot traffic management system for warehouses.
The challenge
As warehouse automation scales, dozens of autonomous robots operating in one space create a congestion problem. Static, pre-programmed routes break down under real loads — and every minute of robot downtime directly cuts logistics throughput. The industry needed traffic management that adapts in real time rather than following fixed paths.
The technology
The funded system predicts warehouse loads in real time, automatically resolves obstacles, and uses machine learning to dynamically optimise robot routes. In effect it is air-traffic control for warehouse robot fleets — continuously learning from the flow it manages.
What we did
ProBaltic Consulting prepared the R&D application — building the case for genuine experimental development in ML-based fleet coordination — and administered the project through implementation.
The result
€135K in non-dilutive R&D funding for a deep-tech product with a global market. The project shows the standard structure of a strong robotics R&D application: a concrete industry bottleneck, a machine-learning approach whose outcome is genuinely uncertain, and measurable performance criteria for the resulting system.
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