RI: Small: Hierarchical Planning, Estimation, and Control for Hybrid Mechanical Systems

RI：小型：混合机械系统的分层规划、估计和控制

基本信息

批准号：
1018167
负责人：
Todd Murphey
金额：
$ 44.96万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-08-01 至 2014-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018167&HistoricalAwards=false
关键词：
RI Small Hierarchical Planning Estimation

项目摘要

Hybrid mechanical systems arise in many applications, including hopping, walking, and climbing robots where contact with the ground changes; skid-steering vehicles where Coulomb friction introduces stick/slip behavior; and prosthetic devices that interact with objects. All of these applications are influenced by nonlinearity, nonsmooth transitions, and uncertainty, and these systems demand new tools in motion planning and control.This work takes advantage of the specific structure of mechanical systems to bound the propagation of uncertainty and to develop feedback controllers that maximize robustness of execution. The work builds on state-of-the-art techniques in motion planning and estimation, including sample-based and optimization-based planning, leading to tools for uncertain hybrid mechanical systems that are analogous to control and estimation tools used for linear systems.Example systems are used to drive algorithm development as well as to verify performance. These include 1) the Monkeybot, a robot that uses electromagnets and a single motor to locomote along a vertical wall;2) a parkour robot that uses mechanical contact and jumping to climb narrow passages; and 3) a skid-steered vehicle that experiences discontinuous dynamics due to stick/slip friction effects. All phases of this work include participation of undergraduates and minority students. In addition to dissemination in conferences and journals, results are disseminated on a publicly viewable wiki.

混合机械系统在许多应用中都出现，包括跳跃，步行和攀爬机器人，与地面变化接触；库仑摩擦引入棍棒/滑动行为的滑动车辆；以及与对象交互的假肢设备。所有这些应用都受到非线性，非平滑过渡和不确定性的影响，这些系统要求在发动计划和控制方面进行新的工具。这项工作利用了机械系统的特定结构来限制不确定性的传播，并开发反馈控制器，以最大程度地提高执行的鲁棒性。这项工作基于运动计划和估算的最新技术，包括基于样本的基于样本和优化的计划，从而为不确定的混合机械系统提供了类似于控制和线性系统的估算工具的工具。示例系统用于驱动算法开发以及验证性能。其中包括1）使用电磁体和单个电动机沿着垂直墙机车的机器人； 2）使用机械接触和跳跃来攀登狭窄通道的跑酷机器人； 3）由于棍子/滑动摩擦效应而导致的不连续动态的滑动车辆。这项工作的所有阶段都包括大学生和少数族裔学生的参与。除了在会议和期刊中的传播外，结果还以公开可见的Wiki传播。