Pattern Formation in Parts Sorting, Feeding, and Assembly

机器人和人类增强计划建议#0308224标题：零件分类、进给和装配中的图案形成PI：Lynch，Kevin西北大学这个项目是研究非缠绕机器人设备(例如，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，以自动(1)分拣零件，(2)定位夹具中的零件(进给)，以及(3)将两个或多个零件带入所需的相对配置(装配)。这类系统的特点是反复摩擦碰撞的复杂动力学。我们用动力系统理论中的定性特征来描述系统动力学行为，如稳定的不动点、极限集、混沌区域等。描述力学的这种抽象使我们能够利用涌现行为，这些涌现行为的存在对力学模型中的不确定性是稳健的，以设计基于传感器的运动计划来稳定期望的分拣、夹具或装配。这项工作的成功将导致形式化的策略，用于工业零件输送和装配系统的自动化设计。我们在传感器设计和基于传感器的控制方面的工作可以应用于其他领域的自组装(例如，在微米或纳米尺度上，低层机制截然不同)。正式的基于传感器的操作规划方法，最初是在机器人学中开发的，可能也适用于这些其他领域。这个项目将通过让本科生参与到研究中来，对本科教育产生影响。该项目还将通过西北大学的IGERT非线性科学课程和由PI教授的ME 449机器人操纵课程来影响研究生教育。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.