Dynamic Parameter Identification of Serial Robots Using a Hybrid Approach

Huang, Yanjiang; Ke, Jianhong; Zhang, Xianmin; Ota, Jun

doi:10.1109/tro.2022.3211194

Cited by 28 publications

(9 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most works, such as in ref. [54], friction is simply modeled as the sum of a viscous and a non-smooth Coulomb torque term. Similar options are chosen in refs.…”

Section: Figure 8 Validation Of Estimated Values Of Motor and Gearbox...mentioning

confidence: 99%

“…[28], the Coulomb friction term is smoothed by an hyperbolic function, so as to avoid a discontinuity at null speed. The same model, with the addition of a Stribeck friction torque is the choice of works [42,54], with the letter requiring the determination of 24 parameters for modeling dissipative forces. Furthermore, 42 parameters are determined in ref.…”

Section: Figure 8 Validation Of Estimated Values Of Motor and Gearbox...mentioning

confidence: 99%

See 1 more Smart Citation

Electro-mechanical modeling and identification of the UR5 e-series robot

Clochiatti,

Scalera,

Boscariol

et al. 2024

Robotica

View full text Add to dashboard Cite

Collaborative robotics is a field of growing industrial interest, within which understanding the energetic behavior of manipulators is essential. In this work, we present the electro-mechanical modeling of the UR5 e-series robot through the identification of its dynamics and electrical parameters. By means of the identified robot model, it is then possible to compute and optimize the energy consumption of the robot during prescribed trajectories. The proposed model is derived from data acquired from the robot controller during bespoke experimental tests, using model identification procedures and datasheet provided by manipulator, motors, and gearbox manufacturers. The entire procedure does not require the use of any additional sensor, so it can be easily replicated with an off-the-shelf manipulator, and applied to other robots of the same family.

show abstract

“…In most works, such as in ref. [54], friction is simply modeled as the sum of a viscous and a non-smooth Coulomb torque term. Similar options are chosen in refs.…”

Section: Figure 8 Validation Of Estimated Values Of Motor and Gearbox...mentioning

confidence: 99%

Section: Figure 8 Validation Of Estimated Values Of Motor and Gearbox...mentioning

confidence: 99%

Electro-mechanical modeling and identification of the UR5 e-series robot

Clochiatti,

Scalera,

Boscariol

et al. 2024

Robotica

View full text Add to dashboard Cite

show abstract

“…Tang et al (2023) proposed a recognition algorithm based on weighted least squares and random weighted particle swarm optimization. Huang et al (2022) proposed an iterative hybrid least square algorithm for parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Research on dynamic parameter identification and collision detection method for cooperative robots

Sun,

Song,

Wang

et al. 2023

View full text Add to dashboard Cite

Purpose The safety performance of cooperative robots is particularly important. This paper aims to study collision detection and response of cooperative robots, which meet the lightweight requirements of cooperative robots and help to ensure the safety of humans and robots. Design/methodology/approach This paper proposes a collision detection, recognition and response method based on dynamic models. First, this paper identifies the dynamic model of the robot. Second, an external torque observer is established based on the model, and a dynamic threshold collision detection method is designed to reduce the interference of model uncertainty on collision detection. Finally, a collision position and direction estimation method is designed, and a robot collision response strategy is proposed to reduce the harm caused by collisions to humans. Findings Comparative experiments are conducted on static threshold and dynamic threshold collision detection, and the results showed that the static threshold only detected one collision while the dynamic threshold could detect all collisions. Conducting collision position and direction estimation and collision response experiments, and the results show that this method can determine the location and direction of collision occurrence, and enable the robot to achieve collision separation. Originality/value This paper designs a dynamic threshold collision detection method that does not require external sensors. Compared with static threshold collision detection methods, this method can significantly improve the sensitivity of collision detection. This paper also proposes a collision position direction estimation method and collision separation response strategy, which can enable robots to achieve post collision separation and improve the safety of cooperative robots.

show abstract

“…However, modeling accuracy of a dynamic model depends on its sensitivity with respect to environmental noise, especially non-Gaussian noise commonly seen in measurements [7]. Many different types of dynamic model identification approaches have been recently proposed, such as least squares (LS)-based methods [8], [9], iterative-based methods [10], [11] and deep learning-based methods [12], [13]. To simplify the dynamic model and also improve the The associate editor coordinating the review of this manuscript and approving it for publication was Ángel F. García-Fernández .…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results of [17] indicate that adding physical constraints can effectively mitigate the overfitting problem which arises during the identification process. Recently, the LMI-SDP technique has been extensively applied to various kinds of parameter identification approaches [10], [11], [18], [19], [20] so as to ensure that the estimated base parameters possess physical consistency. Although the solution-finding efficiency of the LMI-SDP technique can satisfy the needs of most identification approaches, mathematical programing algorithms such as the interior-point method or the ellipsoid method adopted by SDP will become unsuitable when encountering cases in which the identification approaches require a rapid physical feasibility test.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

Huang,

Cheng,

Huang

2023

IEEE Access

View full text Add to dashboard Cite

A rapid recurrent neural network (RNN)-based physical feasibility test algorithm for base parameters during the identification process of a robot dynamic model is proposed in this paper. Firstly, related physical constraints such as inertia tensor, drive chain inertia, and friction are combined into the formulation of linear matrix inequality (LMI) to examine the physical consistency of the base parameters. The optimization problem of LMI is then solved by a matrix-oriented gradient-type RNN. Since the network structure of this type of RNN is simple and the process for solving the optimization problem of LMI is parallel distributed, the physical feasibility test can be completed more quickly than when utilizing commonly used semi-definite programming techniques. By taking advantage of highly efficient computation capabilities, the proposed physical feasibility test algorithm is particularly suitable for identification approaches that require rapid feasibility assessment such as on-line identification methods or optimization-based identification methods. An evolutionary algorithm-based identification method is used as an illustrative example to assess the performance of the proposed approach. In addition, a stability proof for the proposed gradient-type RNN is also provided. Results of the experiments conducted on a 6-DOF industrial robot manipulator verify the effectiveness of the proposed approach.INDEX TERMS Dynamic parameter identification, industrial robot manipulator, physical feasibility, recurrent neural network.

show abstract

Dynamic Parameter Identification of Serial Robots Using a Hybrid Approach

Cited by 28 publications

References 54 publications

Electro-mechanical modeling and identification of the UR5 e-series robot

Electro-mechanical modeling and identification of the UR5 e-series robot

Research on dynamic parameter identification and collision detection method for cooperative robots

A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

Contact Info

Product

Resources

About