Researchers at Stanford University have developed an algorithm that plans and coordinates teams of autonomous robots for modular manufacturing tasks. Published in the journal Robotics and Autonomous Systems, the study presents a system that translates product design plans into optimized assembly workflows, allowing robots to work independently or in teams, manage subassemblies, and move efficiently through shared workspaces.
The algorithm takes input on robot specifications and manufacturing requirements, then produces a coordinated plan that includes station layouts, task assignments, and collision avoidance. This approach aims to increase flexibility and reduce reconfiguration time in modern manufacturing environments that traditionally rely on fixed assembly lines.
“What’s really unusual about what we’re doing here is the scope of the problems we’re solving,” said Mac Schwager, associate professor of aeronautics and astronautics at Stanford and co-author of the paper. “There has been research into some of these individual pieces, but I think we’re the first to really think about how it all fits together into a large-scale system.”
The team demonstrated the algorithm’s efficiency by generating an assembly plan for a toy model of the Saturn V launch vehicle—composed of 1,845 parts and 306 subassemblies—using 250 robots. The full plan was produced in under three minutes.
“Our objective is to go from raw material to the finished product as quickly as possible, and the way you do that is through parallelization,” said Mykel Kochenderfer, associate professor and senior author on the paper. “It’s not a linear sequence – we try to do operations in parallel as frequently as possible.”
The algorithm’s modular strategy allows manufacturing systems to adapt more easily to changes. “Right now, if you want to change your construction pipeline to something different, it requires a lot of planning and work to tear it down and set it back up,” said Dylan Asmar, a PhD student and co-author. “With a more modular approach like this, changing your pipeline would be a lot easier and more streamlined.”
To support further development, the researchers have released an open-source simulator. The platform allows others to test construction algorithms and examine how changes to system constraints affect performance. It has also been used as an educational tool, demonstrating robotics concepts to students.
“There are still plenty of problems to be solved before our work could be used in a real-world manufacturing context,” said lead author Kyle Brown, who began the project as part of his doctoral research.
