Pattern Formation in Parts Sorting, Feeding, and Assembly

题目：零件分拣、馈送和装配中的模式形成[来源：Lynch， kevin]西北大学本项目旨在研究非抓握机器人装置（如振动表面）诱导零件自组装的形状和运动控制策略的设计。这包括设计基于传感器的运动来自动(1)分类零件，(2)定位夹具中的零件（进料）和(3)将两个或多个零件带入所需的相对配置（组装）。这种系统的特点是反复摩擦冲击的复杂动力学。我们从动力系统理论的定性特征，如稳定不动点和极限集、混沌状态等方面描述了系统动力学的行为。这种描述力学的抽象允许我们使用紧急行为，它的存在对力学模型中的不确定性具有鲁棒性，来设计基于传感器的运动计划，以稳定期望的分类、固定或组装。这项工作的成功将为目前通过试错设计的工业零件馈送和装配系统的自动化设计提供正式的策略。我们在传感器设计和基于传感器的控制方面的工作可以在其他领域找到自组装的应用（例如，在微观或纳米尺度上，低级力学是完全不同的）。正式的基于传感器的操作规划方法，最初是在机器人技术中开发的，可能适用于这些其他领域。本项目将对本科生的研究产生影响。该项目还将通过西北大学非线性科学研究中心和PI教授的ME 449机器人操作课程影响研究生教育。该项目的成果将通过项目网站、会议演讲和期刊论文广泛传播。

Robotics and Human Augmentation ProgramABSTRACTProposal #0308224Title: Pattern Formation in Parts Sorting, Feeding, and AssemblyPI: Lynch, KevinNorthwestern UniversityThis project is to study the design of shape and motion control strategies for nonprehensile robotic devices (e.g., vibratory surfaces) inducing self-assembly of parts. This includes designing sensor-based motions to automatically (1) sort parts, (2) orient parts in fixtures (feeding) and (3) bring two or more parts into a desired relative configuration (assembly). Such systems are characterized by the complex dynamics of repeated frictional impacts. We describe the behavior of the system dynamics in terms of qualitative features from dynamical systems theory, such as stable fixed points and limit sets, chaotic regimes, etc. This abstraction of the descriptive mechanics allows us to use emergent behaviors, the existence of which are robust to uncertainties in the mechanics model, to design sensor-based motion plans to stabilize a desired sorting, fixturing, or assembly.Success of this work will lead to formal strategies for automated design of industrial parts feeding and assembly systems that are currently designed by trial and error. Our work on sensor design and sensor-based control could find applications to self-assembly in other domains (e.g., on the micro- or nano-scale where the low-level mechanics are quite different). Formal sensor-based manipulation planning approaches, originally developed in robotics, may be applicable to these other domains.This project will have an impact on undergraduate education by involving undergraduates in the research. The project will also impact graduate education through Northwestern's IGERT on Nonlinear Science and the course ME 449 Robotic Manipulation taught by the PI. Results of the project will be broadly disseminated through project websites, conference talks, and journal papers.