A methodology for joint stiffness identification of serial robots

Dumas, Claire; Caro, Stéphane; Chérif, Mehdi; Garnier, Sébastien; Furet, Benoît

doi:10.1109/iros.2010.5652140

Cited by 42 publications

(40 citation statements)

References 15 publications

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“…With regard to robot joint errors, there are two kinds of models: the MD-H model and the POE model [3][4][5][6][7] , each having their respective scopes of application. Following is a description of the error characteristic indexes of the MD-H model.…”

Section: Joint Errors Of Robotmentioning

confidence: 99%

Research on Robot Joint Error Based on Neural Network

Huang¹,

Liao²,

Chen³

et al. 2017

dtetr

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Since the beginning of the 21 st century, robots have played an important role in both daily life and industrial production. This paper aims to evaluate the types of robot joint errors. First, the types of robot joint errors are described, and the two characteristic indexes, ∆ and ∆ , are identified. Next, robot joint errors are divided into adjustable ones and nonadjustable ones. A robot joint error type model is established using PB neural network model. The areas of adjustable joint errors and nonadjustable joint errors are defined. Finally, reasonable suggestions are given for different types of robot joint errors.Keywords: joint error of robot adjustable and nonadjustable; PB neural network

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Section: Joint Errors Of Robotmentioning

confidence: 99%

Research on Robot Joint Error Based on Neural Network

Huang¹,

Liao²,

Chen³

et al. 2017

dtetr

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show abstract

“…One of them is to identify stiffness by the clamping method, in which the robot is locked in a closed kinematic loop and then exercises joint-based movements while sensing motor position and torque [15]. The identification of the diagonal joint stiffness matrix is also approached by applying external wrenches [16]. The stiffness modeling of heavy industrial robots with gravity compensators has also been studied [17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

et al. 2017

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Optimal and intuitive robotic machining is still a challenge. One of the main reasons for this is the lack of robot stiffness, which is also dependent on the robot positioning in the Cartesian space. To make up for this deficiency and with the aim of increasing robot machining accuracy, this contribution describes a solution approach for optimizing the stiffness over a desired milling path using the free degree of freedom of the machining process. The optimal motion is computed based on the semantic and mathematical interpretation of the manufacturing process modeled on its components: product, process and resource; and by configuring automatically a sample-based motion problem and the transition-based rapid-random tree algorithm for computing an optimal motion. The approach is simulated on a CAM software for a machining path revealing its functionality and outlining future potentials for the optimal motion generation for robotic machining processes.

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“…A first framework for compliant control was proposed by De Schutter and Van Brussel [5], just considering end effector generic compliance. A number of flexible joint rigid link robot models have been then proposed, aiming at parameter identification and force control [6], [7], [8], especially on the basis of the Conservative Congruence Transformation [9]. On the other hand, recent studies on force control were focused on adaptive or alternative control algorithms that can face the uncertainties on environment elasticity [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Implicit force control for an industrial robot with flexible joints and flexible links

Rossi

Bascetta

Rocco

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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The main purpose of this paper is to present an implicit force control scheme for 6 DoF industrial robots, whose compliance model takes into account both joint and link elasticities. The system composed by the robot and the environment is modeled by means of a simple equivalent elasticity at the end effector. A method to avoid limit cycles due to friction during force control applications is then proposed. The compliance model and the force control are experimentally validated on a industrial robot equipped with a force sensor

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A methodology for joint stiffness identification of serial robots

Cited by 42 publications

References 15 publications

Research on Robot Joint Error Based on Neural Network

Research on Robot Joint Error Based on Neural Network

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

Implicit force control for an industrial robot with flexible joints and flexible links

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