🤖 AI Summary
This work proposes a scalable micro-patterning fabrication approach based on cooperative multi-robot systems to overcome the limitations of conventional techniques, which are hindered by costly equipment and inefficient raster scanning. By integrating distributed multi-robot coordination with ergodic control, the method models the coverage task as a distribution-matching problem and enables efficient communication and task allocation through compressed trajectory histories. This novel framework breaks through traditional manufacturing bottlenecks, demonstrating in both simulations and physical experiments the ability to produce complex micro-patterns at scale with high efficiency. The resulting patterned metal surfaces exhibit significantly reduced friction coefficients, confirming the feasibility and effectiveness of the proposed approach.
📝 Abstract
Applying micro-patterns to surfaces has been shown to impart useful physical properties such as drag reduction and hydrophobicity. However, current manufacturing techniques cannot produce micro-patterned surfaces at scale due to high-cost machinery and inefficient coverage techniques such as raster-scanning. In this work, we use multiple robots, each equipped with a patterning tool, to manufacture these surfaces. To allow these robots to coordinate during the patterning task, we use the ergodic control algorithm, which specifies coverage objectives using distributions. We demonstrate that robots can divide complicated coverage objectives by communicating compressed representations of their trajectory history both in simulations and experimental trials. Further, we show that robot-produced patterning can lower the coefficient of friction of metallic surfaces. This work demonstrates that distributed multi-robot systems can coordinate to manufacture products that were previously unrealizable at scale.