In the manufacturing process, each car undergoes 2,000 welds orchestrated by approximately 300 robots. Coordinating multiple robots necessitates an efficient program to distribute tasks and specify movements. Historically, this task relied on human experts, involving extensive trial and error to manually craft programs navigating collisions, preventing interference, and achieving objectives.
Our approach involved crafting a robust objective function, leveraging MakinaRocks' AI experts' domain knowledge to encapsulate real-world manufacturing constraints. Integrating machine learning and optimization techniques, we automated task assignments to individual robots. Advanced path planning algorithms were then employed for swift and secure robot program generation aligned with assigned tasks. Simultaneously, our strategy supported module parallelization, enhancing operational speed and path exploration efficiency.
The automated generation of robot programs not only efficiently executed specific processes on production lines featuring multiple robots but also drastically compressed traditional timelines from months to just three days. This achievement resulted in a substantial acceleration of product production, contributing to a significantly shortened overall production cycle.
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