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EAGER: Fundamental Methods for Design, Control, and Motion Planning of Soft Robots

EAGER：软机器人设计、控制和运动规划的基本方法

基本信息

批准号：
1551219
负责人：
Cagdas Onal
金额：
$ 24.74万
依托单位：
Worcester Polytechnic Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2018-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551219&HistoricalAwards=false
关键词：
EAGER Fundamental Methods Design Control

项目摘要

The next frontier for robotics is bringing robots into environments such as homes, flexible manufacturing, and disaster sites, where the standard robot design, consisting of rigid links actuated by electric motors, limits their usefulness and creates safety concerns. Unlike rigid robots, soft robots have the potential to be highly accessible, versatile, and inherently safe around humans. However, there is currently a lack of engineering principles for designing soft robots that perform practical tasks. It is also unclear how to enable these robots to move reliably and efficiently in cluttered environments, like a disaster site. This work explores new methods for designing, controlling, and planning motion for soft robot arms.The technical objective is to explore, formalize, and validate the fundamental algorithmic and engineering principles necessary to build soft robot arms and allow them to perform useful tasks. To that end, the project investigates (1) A modular soft arm design where each module can offer a combination of 3D bending, extension, and change in stiffness, (2) Modeling and control methods to achieve reliable position, force, and impedance control, and (3) Motion planning algorithms which plan in the belief space of the robot and exploit contact with the environment to overcome uncertainty in the motion model.

机器人学的下一个前沿是将机器人带入家庭、灵活制造和灾难现场等环境中，在这些环境中，标准的机器人设计(由电动马达驱动的刚性连杆组成)限制了它们的可用性，并造成了安全问题。与刚性机器人不同，软机器人具有高度可访问性、多功能性以及在人类周围本质上安全的潜力。然而，目前还缺乏用于设计执行实际任务的软机器人的工程原理。目前也不清楚如何让这些机器人在混乱的环境中可靠而高效地移动，比如灾难现场。这项工作探索了设计、控制和规划软机械臂运动的新方法，其技术目标是探索、形式化和验证建立软机械臂并允许它们执行有用任务所必需的基本算法和工程原理。为此，该项目研究了(1)模块化软臂设计，其中每个模块可以提供3D弯曲、延伸和刚度变化的组合；(2)建模和控制方法，以实现可靠的位置、力和阻抗控制；以及(3)运动规划算法，其在机器人的信念空间中进行规划，并利用与环境的接触来克服运动模型中的不确定性。