Manufacturing Micro-Patterned Surfaces with Multi-Robot Systems
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
3D Modular Microrobots: Micro-Origami Cubes with Integrated Si Chips Dive, Communicate, Flash Programs, and Form Collectives
Modular microrobotics can potentially address many information-intensive microtasks in medicine, manufacturing and the environment. However, surface area has limited the natural powering, communication, functional integr…
FormDesign, analysis, and manufacturing of a glass-plastic hybrid minimalist aspheric panoramic annular lens
We propose a high-performance glass-plastic hybrid minimalist aspheric panoramic annular lens (ASPAL) to solve several major limitations of the traditional panoramic annular lens (PAL), such as large size, high weight, a…
Control Architecture and Design for a Multi-robotic Visual Servoing System in Automated Manufacturing Environment
The use of robotic technology has drastically increased in manufacturing in the 21st century. But by utilizing their sensory cues, humans still outperform machines, especially in micro scale manufacturing, which requires…
DEEP$^2$: Deep Learning Powered De-scattering with Excitation Patterning
Limited throughput is a key challenge in in-vivo deep-tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the wide-field ima…
Deep LearningProbing Three-dimensional Collective Cancer Invasion with DIGME
Multicellular migration and pattern formation play important roles in developmental biology, cancer metastasis and wound healing. To understand the collective cell dynamics in three dimensional extracellular matrix (ECM)…