Force Control in Robotics

Villani, Luigi

doi:10.1007/978-1-4471-5102-9_169-1

Cited by 3 publications

(9 citation statements)

References 17 publications

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“…Our approach can also be implemented on a position-controlled robot, provided that it is equipped with a force sensor, allowing compliant motion by generating the position reference online based on the sensed force [4]. …”

Section: Discussionmentioning

confidence: 99%

“…For tasks involving free motion and simpler tasks involving contact, this may be sufficient. However, for tasks requiring specific forces to be exerted on the environment, or tasks involving contact as important elements, the use of stiff position control is insufficient [4].…”

Section: Related Workmentioning

confidence: 99%

“…In our previous work [7], we proposed to use the perturbations in a time-window 4 [t−S, t] for determining the stiffness at time t. In this paper, we incorporate a preparatory band-pass filtering step before proceeding to computing the stiffness using the filtered signal. The role of this filter is two-fold.…”

Section: Stiffness Decrease Based On Imposed Perturbationsmentioning

confidence: 99%

“…Regardless of how the physical compliance is achieved (joint space/task space and active/passive), the compliance should be chosen to suit the needs of the task at hand. This is often a hard problem, and previous approaches typically need accurate models of the interaction environment [4], [5], [6]. Furthermore, the task may require that these parameters change during the task execution.…”

Section: Introductionmentioning

confidence: 99%

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Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction

Kronander

Billard

2014

IEEE Trans. Haptics

139

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Abstract-Robot Learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use learning to model the kinematics of the task, and for autonomous execution the robot then relies on a stiff position controller. While many tasks can and have been learned this way, there are tasks in which controlling the position alone is insufficient to achieve the goals of the task. These are typically tasks that involve contact or require a specific response to physical perturbations. The question of how to adjust the compliance to suit the need of the task has not yet been fully treated in Robot Learning from Demonstration. In this paper, we address this issue and present interfaces that allow a human teacher to indicate compliance variations by physically interacting with the robot during task execution. We validate our approach in two different experiments on the 7 DoF Barrett WAM and KUKA LWR robot manipulators. Furthermore, we conduct a user study to evaluate the usability of our approach from a non-roboticists perspective.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Stiffness Decrease Based On Imposed Perturbationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction

Kronander

Billard

2014

IEEE Trans. Haptics

139

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“…For example, if the task of the target robot is to write something, neglecting control of the interaction force may lead to either loss of contact or hard pressure on the target environment [ 1 ]. In general, for rigid or dynamic interaction environments, pure position control schemes are not recommended, especially if the environment is stiff; the contact forces may reach unsafe values [ 2 ] In addition, the robot loses some degrees of freedom (DoFs) during the contact phase. Consequently, the generalized coordinates of the target robot might be larger than its DoFs due to its constrained motion; this constitutes a closed-chain mechanism with redundant coordinates [ 3 ] The robot may change its configuration during a transition from an open-chain mechanism to a closed-chain mechanism.…”

Section: Introductionmentioning

confidence: 99%

Active Impedance Control of Bioinspired Motion Robotic Manipulators: An Overview

Al-Shuka

Leonhardt

Zhu

et al. 2018

Applied Bionics and Biomechanics

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There are two main categories of force control schemes: hybrid position-force control and impedance control. However, the former does not take into account the dynamic interaction between the robot's end effector and the environment. In contrast, impedance control includes regulation and stabilization of robot motion by creating a mathematical relationship between the interaction forces and the reference trajectories. It involves an energetic pair of a flow and an effort, instead of controlling a single position or a force. A mass-spring-damper impedance filter is generally used for safe interaction purposes. Tuning the parameters of the impedance filter is important and, if an unsuitable strategy is used, this can lead to unstable contact. Humans, however, have exceptionally effective control systems with advanced biological actuators. An individual can manipulate muscle stiffness to comply with the interaction forces. Accordingly, the parameters of the impedance filter should be time varying rather than value constant in order to match human behavior during interaction tasks. Therefore, this paper presents an overview of impedance control strategies including standard and extended control schemes. Standard controllers cover impedance and admittance architectures. Extended control schemes include admittance control with force tracking, variable impedance control, and impedance control of flexible joints. The categories of impedance control and their features and limitations are well introduced. Attention is paid to variable impedance control while considering the possible control schemes, the performance, stability, and the integration of constant compliant elements with the host robot.

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Active Manipulation

Kappassov

2021

Encyclopedia of Robotics

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Force Control in Robotics

Cited by 3 publications

References 17 publications

Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction

Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction

Active Impedance Control of Bioinspired Motion Robotic Manipulators: An Overview

Active Manipulation

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