Siemens & TRUMPF Team Up for AI-Driven Industrial Robot Systems
Siemens and TRUMPF are teaming up to create AI-driven industrial robot systems that integrate digital manufacturing and interoperable IT and OT systems. Meanwhile, Circus SE is producing autonomous AI-robot systems at an industrial scale. The goal? To enable robots to learn, react, and cooperate autonomously.
Currently, the system is not yet deployed in real production lines, facing challenges like handling robots with varying capabilities and tasks requiring specific orders. The vision is for humans to define tasks at a high level, with the system autonomously determining the optimal sequence, action assignment, and collision-free paths for all robots.
Torsten Kroeger, Chief Science Officer at Intrinsic, sees this technology as a 'critical step' towards high-efficiency planning in manufacturing. Researchers from Google DeepMind, Intrinsic, and UCL have developed 'RoboBallet', an AI that coordinates multiple industrial robots collision-free and efficiently. The AI learns optimized movement paths in millions of synthetic scenes, requiring only CAD files and a rough task description. In lab tests, it outperformed traditional methods and expert-designed solutions by about 25% in efficiency.
With 4.3 million industrial robots worldwide, programming them takes over 100 million hours, often lost during task or layout changes. The AI method uses a Graph Neural Network (GNN) trained via Reinforcement Learning to model relationships between robots, tasks, and obstacles as 'nodes' in a graph. Efficiency scales with the number of robots; increasing from four to eight robots reduced the average task execution time by 60%.
Siemens, TRUMPF, and Circus SE are pushing the boundaries of industrial robotics with AI. While challenges remain, the potential to revolutionize manufacturing through autonomous, efficient robot coordination is clear. As the technology advances, it could significantly reduce programming time and improve productivity.
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