使用集成的主动和被动柔性控制方法完成复杂的接触任务

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9062
Adam Pettinger, M. Pryor
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引用次数: 3

摘要

介绍了在柔性机械臂上实现的广义接触控制框架(GCCF)。我们证明了联合柔度控制和基于gccf的柔度控制能够在不确定环境中完成复杂的接触任务,其中复杂是指需要满足涉及多个步骤和输出轴的不同接触力要求。在不确定的环境中操作意味着对接触物体的位置或材料特性的了解有限。演示的任务包括打开药瓶,并将其刚性连接到纯机械工具更换器上。GCCF简化了接触控制参数的定义和修改,并允许动态定义和完成新任务。与混合力/阻抗控制器不同,我们不需要定义大的阻尼和刚度矩阵,并且我们将关节电平控制增益与柔度控制解耦。其结果是一个可以在无约束运动和接触任务之间动态切换的机器人机械手,并提供了许多通用性来执行各种任务。
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Completing Complex Contact Tasks Using Integrated Active and Passive Compliant Control Methodologies
In this paper we introduce the Generalized Contact Control Framework (GCCF) implemented on a compliant robotic manipulator. We demonstrate that the combined joint compliance and GCCF-based compliance control enable the completion of complex contact tasks in uncertain environments, where complex refers to the need to meet different contact force requirements involving multiple steps and output axes. Operating in uncertain environments means limited knowledge of the location or material properties of contact objects. The demonstrated tasks include opening a pill bottle and rigidly connecting to a purely mechanical tool changer. The GCCF simplifies the definition and modification of contact control parameters and allows for on-the-fly definition and completion of new tasks. Unlike hybrid force/impedance controllers, we do not need to define large damping and stiffness matrices, and we decouple the joint level control gains from the compliance control. The result is a robotic manipulator that can dynamically switch between unconstrained motion and contact tasks and provides a lot of versatility to perform a wide variety of tasks.
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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