Stigmeric Coverage with a Robotic Collective for Surface Micro-patterning

Micro-structured surfaces enable beneficial properties including drag reduction and hydrophobicity. However, a lack of affordable and efficient manufacturing techniques prevent their widespread use. Currently, applying micro-scale divots to meter-scale surfaces requires expensive tooling to create evenly spaced indentations while supporting large workpieces. Here, we use mobile robots with credit-card-sized footprints for surface coverage to achieve high-fidelity patterns from detailed target images, patterning according to feature density instead of traditional feature specifications that require precision. In this work, we parallelize surface coverage with multiple decentralized robotic agents that indirectly communicate through stigmergy, a principle of coordination through altering and detecting signatures left by other agents in an environment. By sensing local feature density and comparing with a desired density function over the surface, robots can determine whether to prioritize texturing their immediate environment or cover area elsewhere. With this method, robots can collaborate to recreate detailed target images based on local information and indirect communication, providing an alternative manufacturing paradigm for surface texturing that is flexible and adaptable.