ROBOT x Manufacturing

Large scale surface patterning of micro-structured functional surfaces using a cohort of robots.

Micro-structured surfaces possess useful properties such as friction modification, anti-fouling, and hydrophobicity. However, manufacturing these surfaces in an affordable, scalable, and efficient manner remains challenging. Standard coverage methods for surface patterning require precise placement of micro-scale features over meter-scale surfaces with expensive tooling for support. These works address the scalability challenge in surface patterning through a team of mobile robots. We’ve demonstrated that credit-card-sized “minions” can generate micro-scale divots using a modulated tool tip to pattern meter-scale workpieces, or use a sanding actuator to reduce surface roughness. We show that an Ergodic Control architecture with a target based on feature density can specify surface coverage, eliminating the dependence on individual indentation locations. Our robots automatically plan their trajectories to produce high-fidelity surface patterns and achieve automatic coverage based on sophisticated target images. Robots can either work independently, or as a team with shared or cascaded trajectory histories to divide tasks. We’ve validated robot performance by controlling the coefficient of friction in a submerged lubrication regime by adjusting the density of indentations, and by producing various recognizable images including the Eiffel Tower and Arc de Triomphe. These works show the potential for compact robots to perform scalable manufacturing of functional surfaces enabled by density-based target specifications (Taylor et al., 2024; Landis et al., 2025; Taylor et al., 2026; Landis et al., 2026).

(in collaboration with Prof. Todd Murphey)

Distributed Surface Patterning Through a Cohort of Robots

References

2026

Manufacturing Micro-Patterned Surfaces with Multi-Robot Systems

Annalisa T. Taylor , Malachi Landis , Ping Guo , and Todd D. Murphey

arXiv preprint arXiv:2603.18260, Oct 2026

Abstract arXiv PDF Video

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.
Collaborative surface patterning through cascaded ergodic control and automatic task allocation

Malachi Landis , Annalisa T. Taylor , Todd D. Murphey , and Ping Guo

Journal of Manufacturing Systems, Oct 2026

Abstract PDF

Coordinating groups of heterogeneous agents to accomplish multiple sequential surface patterning objectives is challenging and may be difficult or impossible to achieve manually when the number of agents does not evenly partition the tasks. One important sequential surface patterning task is the application of friction-reducing textures, where beneficial indentations are first formed in a surface then the resulting raised burr is removed via polishing, producing a surface with lower sliding friction under submerged lubrication. Here, we introduce cascaded ergodic control to enable automatic sequential surface modification by teams of mobile robots. The trajectory history of a group of leader robots is taken as the target for a group of follower robots, allowing implicit task allocation without explicit sharing of task information. We demonstrate, in both simulation and experiment, that this method matches the performance of existing ergodic control schemes and enables flexible assignment of agents to different tasks without directly communicating objectives to follower robots. Teams of indentation and polishing robots work together in physical experiments to pattern acrylic workpieces, demonstrating a process scalable to patterning of massive workpieces like airplanes or cargo ships with greater flexibility than existing methods.

2025

Large-scale functional patterning using mobile robot swarms and ergodic control

Malachi Landis , Muye Jia , Annalisa T. Taylor , Todd D. Murphey , and Ping Guo

CIRP Annals, Oct 2025

Abstract PDF Video

Large-scale surfaces, such as ship hulls, face challenges in applying functional structures due to manufacturing constraints. This work presents a scalable approach using low-cost, autonomous robot swarms guided by an ergodic control framework to pattern micro-scale features on meter-scale metallic surfaces. The robots operate collaboratively to achieve density-specific coverage, optimizing surface functionalities like friction reduction and hydrodynamic performance. Experimental validation demonstrates the critical role of feature density in tribological performance, and an example application highlights the creation of gradient-density patterns. This innovative method unlocks new possibilities for functional surface patterning on large-scale structures with imprecise robots.

2024

Image to patterning: density-specified patterning micro-structured surfaces with a mobile robot

Annalisa T. Taylor , Malachi Landis , Yaoke Wang , Todd D. Murphey , and Ping Guo

In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct 2024

Abstract PDF Video

Micro-structured surfaces possess useful properties such as friction modification, anti-fouling, and hydrophobicity. However, manufacturing these surfaces in an affordable, scalable, and efficient manner remains challenging. Standard coverage methods for surface patterning require precise placement of micro-scale features over meter-scale surfaces with expensive tooling for support. In this work, we address the scalability challenge in surface patterning by designing a mobile robot with a credit-card-sized footprint to generate micro-scale divots using a modulated tool tip. We provide a control architecture with a target feature density to specify surface coverage, eliminating the dependence on individual indentation locations. Our robot produces high-fidelity surface patterns and achieves automatic coverage of a surface from sophisticated target images. We validate an exemplary application of such micro-structured surfaces by controlling the friction coefficients at different locations according to the density of indentations. These results show the potential for compact robots to perform scalable manufacturing of functional surfaces, switching the focus from precision machines to small-footprint devices tasked with matching only the density of features.