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NRI: FND: Dexterous Manipulation Using Multi-Serial Manipulator Systems with Real-Time Compliance Modulation

NRI：FND：使用具有实时顺应性调制的多串行机械手系统进行灵巧操纵

基本信息

批准号：
2024554
负责人：
Joseph Schimmels
金额：
$ 75万
依托单位：
Marquette University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024554&HistoricalAwards=false
关键词：
NRI FND Dexterous Manipulation Using

项目摘要

This National Robotics Initiative project will promote the progress of science and advance the national health, prosperity and welfare by discovering foundational scientific knowledge needed to allow robots to achieve human-like dexterity in constrained manipulation tasks. Current robots are better than people at executing desired motions in free space, but they are not nearly as adept at performing tasks in which motion is constrained in some way. Examples of such tasks in everyday life include peeling potatoes, opening a bottle, closing a container, cleaning a surface, or assembling furniture. Robots have great difficulty in performing these types of tasks because inevitable uncertainties in object exact locations often cause robot commanded positions to conflict with physical constraints, resulting in excessive contact forces and task failure. This award supports fundamental research into improving robot dexterity by providing a robot system the human-like ability to continuously adjust its inherent mechanical behavior as a task progresses so that it can be compliant in directions that are constrained and to be stiff in directions for which the commanded motion is needed to complete the task. Human-like dexterous manipulation is needed for robots to be widely used in senior living-assistance, agriculture, construction, space exploration, healthcare, nuclear remediation, and manufacturing. The robustness of the approach will be demonstrated in a manufacturing assembly testbed application. Therefore, results from this research will provide broad benefit to society and the U.S. economy. The technologies developed will be in the areas of mechanical engineering (kinematics, dynamics, and control) and computer science and will engage and educate students, including members of underrepresented groups, in these areas of national need.To obtain improved dexterity, multiple collaborating serial manipulators will be used to achieve a desired time-varying compliance, one specifically optimized for a given task so that it is properly executed despite uncertainties in task geometry and associated physical constraints. The compliance will be passively realized using variable stiffness actuators in each joint of the multi-manipulator system. With this optimized modulated passive compliance approach, the inherent mechanical behavior of the robot system is guaranteed to regulate contact force to not exceed specified limits and to move as desired to attain task objectives. The three specific aims of this research project are: 1) identify necessary and sufficient conditions and general synthesis procedures to achieve any specified passive compliance for different multi-serial manipulator topologies, 2) identify procedures for continuously and simultaneously attaining the desired object position and passive elastic behavior as an object is moved by redundant collaborating robots for different multi-serial manipulator topologies, and 3) demonstrate customizable dexterous manipulation using a three-finger planar robot hand with modulated passive compliance (using novel antagonistic tendon-driven variable stiffness actuators) performing a variety of different planar assembly tasks quickly and reliably.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个国家机器人计划项目将通过发现基础科学知识来促进科学的进步，促进国家的健康、繁荣和福利，这些基础科学知识需要让机器人在受限的操作任务中实现像人类一样的灵活性。目前的机器人在自由空间中比人类更擅长执行期望的动作，但它们在执行运动在某种程度上受到限制的任务时就不那么熟练了。日常生活中这样的任务包括削土豆皮、打开瓶子、关闭容器、清洁表面或组装家具。机器人在执行这些类型的任务时遇到了很大的困难，因为物体精确位置的不可避免的不确定性常常导致机器人命令位置与物理约束相冲突，从而导致过大的接触力和任务失败。该奖项支持提高机器人灵巧性的基础研究，为机器人系统提供类似人类的能力，使其能够随着任务的进展不断调整其固有的机械行为，以便它可以在受限的方向上顺应，并在完成任务所需的命令运动方向上保持僵硬。机器人要广泛应用于老年生活辅助、农业、建筑、太空探索、医疗保健、核修复和制造业等领域，就需要类似人类的灵巧操作。该方法的健壮性将在一个制造装配测试平台应用程序中得到证明。因此，这项研究的结果将为社会和美国经济提供广泛的利益。开发的技术将在机械工程（运动学、动力学和控制）和计算机科学领域，并将参与和教育学生，包括代表性不足的群体成员，在这些国家需要的领域。为了提高灵活性，将使用多个协作串行机械手来实现期望的时变顺应性，其中一个针对给定任务进行了专门优化，以便在任务几何形状和相关物理约束不确定的情况下正确执行。在多机械臂系统的各个关节中采用变刚度作动器被动实现柔度控制。采用这种优化的调制被动顺应方法，保证了机器人系统的内在机械行为可以调节接触力不超过规定的限制，并按照期望的方式移动以达到任务目标。本研究项目的三个具体目标是：1)确定了不同多串联机械臂拓扑结构下实现任意指定被动柔度的充分必要条件和一般综合程序；2)确定了不同多串联机械臂拓扑结构下冗余协作机器人连续同时实现目标位置和目标移动时被动弹性行为的程序；3)演示可定制的灵巧操作，使用具有调制被动顺应性的三指平面机器人手（使用新型拮抗肌腱驱动的可变刚度执行器）快速可靠地执行各种不同的平面装配任务。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。