Manufacturing Micro-Patterned Surfaces with Multi-Robot Systems
Annalisa T. Taylor, Malachi Landis, Ping Guo, Todd D. Murphey
AI summary
Problem
Existing manufacturing techniques struggle to produce micro-patterned surfaces at scale due to high costs and inefficient raster-scanning, despite the fact that surface functionality depends on feature density rather than exact placement.
Approach
The authors deploy multiple mobile robots with indentation tools that apply micro-dimples based on target density distributions. Robots coordinate by sharing trajectory history via a decentralized ergodic control algorithm to divide coverage tasks efficiently.
Key results
- Developed a scalable multi-robot micro-patterning technique using density specifications
- Demonstrated that robot-produced patterns reduce sliding friction on metallic surfaces
- Showed that decentralized communication enables effective task decomposition among robots
- Verified density control and friction reduction through simulation and hardware experiments
Why it matters
Enables scalable, low-cost manufacturing of functional surfaces for applications like ship hulls and industrial components, bypassing the need for expensive precision equipment.
Abstract
Applying micro-patterns to surfaces has been shown to impart useful physical properties such as drag reduc- tion and hydrophobicity. However, current manufacturing tech- niques 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.