Stiffness Identification for Serial Robot Manipulator Based on Uncertainty Approach

The paper focuses on the identification of elastostatic properties of an industrial serial robot mounted on a rail. It proposes an identification procedure in order to find the optimal robot configuration to minimize the impact of measurement errors on the identification accuracy of the stiffness parameters. An experimental setup is designed to perform the identification of all stiffness parameters under industrial conditions. The proposed identification procedure is easy to use and takes little time.

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“…Equations (14) to (16) give the joint angles vectors associated with the three optimal measurement poses.…”

Section: Optimisation Resultsmentioning

confidence: 99%

Optimal measurement pose selection for joint stiffness identification of an industrial robot mounted on a rail

Guérin

Caro

Garnier³

et al. 2014

2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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“…Since τ J is sensed by most robots, such as KUKA LWR III, 4+ and IIWA, as mentioned in [20], optimized dynamics parameters are those involved in equation (2a). Additional drive train parameters are obtained from the literature, such as from [23] for joint stiffnesses in our KUKA KR 16 model in Fig. 6(b) and [20] for the gear-reduction range in our LWR /IIWA models.…”

Section: A Robot Dynamics Capturementioning

confidence: 99%

Value-Driven Robotic Digital Twins in Cyber–Physical Applications

Kaigom

Rosmann

2021

IEEE Trans. Ind. Inf.

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Although the skills of robot manipulators are becoming technically more complex, the unprecedented costeffective access to recently unveiled intelligent robots has the potential to unleash as yet unimagined automation capabilities. A key technology behind this opportunity for companies to gain a competitive edge through an informed and intelligent robotized automation is the robotic digital twin (RDT). As such, the RDT will be instrumental in mirroring targeted properties of a physical robot to obtain a digital sibling flexibly harnessed in virtual testbeds to understand, predict, and shape the robot performance. However, these objectives remain challenging to well-established simulators. This is because the architectural and functional capabilities they support are not sufficiently in-line with ever-growing and varying demands for agile and cost-efficient manipulations. As a consequence, robot stakeholders can hardly use RDTs to unlock opportunities and meet needs from prospective markets. This paper contributes to addressing this gap. We introduce a novel concept for the development of a RDT that helps create and add value to current and future robotized cyber-physical applications. Hereinafter referred to as the value-driven RDT (vdRDT), it systematically captures the robot dynamics and purposefully farms data, about which its services reason, to facilitate insight and deliver capabilities as well as benefits to stakeholders. Experiment results show that vdRDTs enlarge the scope of, adapt to, and revitalize robotized applications carried out in different fields.

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“…Then researchers continued to develop an alternative approach, which is based on the analysis of joint stiffness and the calculation of joint deformation through joint stiffness. XP Zhang et al [7], CT Mao et al [8], Guo Yingjie et al [9], Lin Y et al [10] and Klimchi [11] have succeed to model the errors of different robots by this approach, and results showed that this approach is good in establishing the connection between robot joints and end deformation errors.…”

Section: Introductionmentioning

confidence: 99%

A study of KUKA robot joint error modeling and experimental verification

Zhang

Li²,

Qiu³

et al. 2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

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Industrial robots have great advantages in the processing of large and complex components in the aerospace field, but the lack of robot joint stiffness results in poor processing accuracy. This paper first analyzes the stiffness of the robot's joints and establishes a joint error model; Secondly, the kinematics modeling of the KUKA KR 600 robot was carried out by using the Modified D-H method, the established model was calibrated by MATLAB, and the Jacobian matrix J was calculated; Thirdly, the stiffness of the robot joints was identified through experiments; Finally, the joint error model validation was carried out. Results showed that the relative errors between the predicted and actual measured values in the x, y and z directions are 21.39%, 17.01% and 14.46% respectively. It is proved that the established joint error model shows large potential in predicting the deformation of the robot end.

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Stiffness Identification for Serial Robot Manipulator Based on Uncertainty Approach

Cited by 10 publications

References 7 publications

Optimal measurement pose selection for joint stiffness identification of an industrial robot mounted on a rail

Optimal measurement pose selection for joint stiffness identification of an industrial robot mounted on a rail

Value-Driven Robotic Digital Twins in Cyber–Physical Applications

A study of KUKA robot joint error modeling and experimental verification

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