Pattern Formation in Parts Sorting, Feeding, and Assembly

机器人和人类增强计划摘要提案#0308224标题：零件分类，进给和装配中的模式形成PI：Lynch，KevinNorthwestern University该项目是研究非可移动机器人设备（例如，振动表面）引起部件的自组装。 这包括设计基于传感器的运动，以自动（1）分类零件，（2）在夹具中定位零件（进给），以及（3）将两个或更多个零件带入所需的相对配置（装配）。 这种系统的特点是反复摩擦碰撞的复杂动力学。 我们从动力系统理论的定性特征方面描述系统动力学的行为，例如稳定的不动点和极限集，混沌状态等。这种描述性力学的抽象允许我们使用涌现行为，其存在对力学模型中的不确定性具有鲁棒性，以设计基于传感器的运动计划来稳定期望的分选，固定，这项工作的成功将导致工业零件供给和装配系统的自动化设计的正式战略，目前设计的试错。 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，在微观或纳米尺度上，其中低级机制是相当不同的）。 正式的基于传感器的操作规划方法，最初在机器人技术中开发的，可能适用于这些其他domines.This项目将有本科生教育的影响，涉及本科生的研究。 该项目还将通过西北大学的IGERT非线性科学和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.