Mobile autonomous robots have the potential to revolutionize manufacturing processes. However, employing large robot fleets in manufacturing requires addressing challenges including collision-free movement in a shared workspace, effective multi-robot collaboration to manipulate and transport large payloads, complex task allocation due to coupled manufacturing processes, and spatial planning for parallel assembly and transportation of nested subassemblies. We propose a full algorithmic stack for large-scale multi-robot assembly planning that addresses these challenges and can synthesize construction plans for complex assemblies with thousands of parts in a matter of minutes. Our approach takes in a CAD-like product specification and automatically plans a full-stack assembly procedure for a group of robots to manufacture the product. We propose an algorithmic stack that comprises: (i) an iterative radial layout optimization procedure to define a global staging layout for the manufacturing facility, (ii) a graph-repair mixed-integer program formulation and a modified greedy task allocation algorithm to optimally allocate robots and robot sub-teams to assembly and transport tasks, (iii) a geometric heuristic and a hill-climbing algorithm to plan collaborative carrying configurations of robot sub-teams, and (iv) a distributed control policy that enables robots to execute the assembly motion plan collision-free. We also present an open-source multi-robot manufacturing simulator implemented in Julia as a resource to the research community, to test our algorithms and to facilitate multi-robot manufacturing research more broadly. Our empirical results demonstrate the scalability and effectiveness of our approach by generating plans to manufacture a LEGO model of a Saturn V launch vehicle with 1845 parts, 306 subassemblies, and 250 robots in under three minutes on a standard laptop computer.

The move from automated to autonomous process manufacturing is right around the corner. This article comes from the May 2021 issue of Intech Focus: Process Control and Safety. For process manufacturing, the ultimate promise of Industry 4.0 is autonomous manufacturing. Autonomous control of manufacturing processes is required, not to eliminate human workers, but to build resilient and highly responsive manufacturing supply chains. Resilience is required to enhance the top and bottom lines of a manufacturing enterprise.

For decades, industrial control systems have been generating enormous volumes of data, but in many cases that data hasn't been fully employed to help companies reduce operating costs, improve reliability, and increase productivity--three goals that amount to the holy grail of manufacturing. Until recently, the path forward has been blocked by insufficient compute power, storage, and machine learning technologies to allow companies to harness the richness of the data they generate. Today, all of this is changing. Thanks to the rise of the Industrial Internet of Things, dramatic advances in computing systems, and the rapid maturation of machine learning algorithms, manufacturers now have the ability to collect, store, and analyze huge amounts of data in real time to turn it into actionable information. They can use this information to enhance operations, remedy equipment issues proactively, improve plant availability, and meet countless other goals that drive toward better margins for the business.