Dynamic modeling and system identification of internally actuated, small-sized continuum robots

Norouzi-Ghazbi, Somayeh; Janabi–Sharifi, Farrokh

doi:10.1016/j.mechmachtheory.2020.104043

Cited by 19 publications

(10 citation statements)

References 26 publications

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“…The models have different performances in terms of accuracy and computation time depending on the loading scenarios and robot design. One interesting observation is that it is not necessary for the tendon path assumption to reflect the actual TDCR design, confirming what was already proposed by Rucker and Webster, 2011a ; Norouzi-Ghazbi and Janabi-Sharifi (2020) ; Barrientos-Diez et al (2021) . Models assuming fully constrained tendons, such as the VC or CC models, can still accurately describe the shape of a TDCR with spacer disks when sufficient number disks is used.…”

Section: Discussionsupporting

confidence: 83%

“…However, a static model considering fully constrained tendons is proved to be accurate in predicting the shape of a TDCR composed of spacer disks in Rucker and Webster (2011a) . Similarly, Norouzi-Ghazbi and Janabi-Sharifi (2020) use the partially constrained tendon path assumption to model a TDCR whose tendons are guided through lumen.…”

Section: Comparison Of Tdcr Models and Guidelinesmentioning

confidence: 99%

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How to Model Tendon-Driven Continuum Robots and Benchmark Modelling Performance

et al. 2021

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Tendon actuation is one of the most prominent actuation principles for continuum robots. To date, a wide variety of modelling approaches has been derived to describe the deformations of tendon-driven continuum robots. Motivated by the need for a comprehensive overview of existing methodologies, this work summarizes and outlines state-of-the-art modelling approaches. In particular, the most relevant models are classified based on backbone representations and kinematic as well as static assumptions. Numerical case studies are conducted to compare the performance of representative modelling approaches from the current state-of-the-art, considering varying robot parameters and scenarios. The approaches show different performances in terms of accuracy and computation time. Guidelines for the selection of the most suitable approach for given designs of tendon-driven continuum robots and applications are deduced from these results.

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Section: Discussionsupporting

confidence: 83%

Section: Comparison Of Tdcr Models and Guidelinesmentioning

confidence: 99%

How to Model Tendon-Driven Continuum Robots and Benchmark Modelling Performance

et al. 2021

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“…For a joint type robot with strong nonlinearity, the robot's dynamic parameters maybe change under different tasks. If the load identification and control parameters are not adjusted to the change, it will cause unexpected vibration or abnormal wear [15,16].…”

Section: Introduction 1motivationsmentioning

confidence: 99%

A workload identification method of industrial robot combining dynamic model and convolutional neural network

Yue,

Wang,

Zhang

et al. 2024

Eng. Res. Express

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The motion joint system of industrial robot has obvious nonlinear characteristics, high dimensional running data and limited number of experimental samples. If mature neural network algorithm is used for identifying the workload of robots, it is easy to have over-learning problems, which seriously restricts the generalization ability of load identification method. In this paper, a load identification method combining the dynamic model of industrial robots with the neural network data model is proposed. A UR5 robot is used for carrying out multiple dynamic load testings to verify the effectiveness of the proposed identification method. Firstly, the workload identification by CNN algorithm is given, and the influence of parameters of prediction model and different neutral network are analyzed. Then the classical dynamic model of industrial robots with multi-degrees of freedom is established. The identified workload by dynamic model is also analyzed. At last, the deterministic information such as velocity and displacement is extracted from the calculation results of the dynamic model as the initial anchoring value. Then convolutional neural network is applied for compensating the residual highly nonlinear information. An improved mixing combination method is also proprosed. This method can effectively deal with the interaction of different types of information in the data, and preliminarily cooperate the dynamic model and convolutional neural network. This provides a basic framework and method for solving the problem of parameter identification in multi-degree-of-freedom systems with small samples.

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“…Moreover, reducing the assembly time and weight of the device, and increasing the adaptability of the RACS to a broader range of catheters is of interest. The applications of Althea II are twofold, (1) development of the set-up for clinical applications with a main focus on catheter-based cardiovascular interventions, and (2) being served as a test-bed for some of the hot research areas in the field including modeling verifications, 15 implementation of various control systems, 16,17 simultaneous control of some catheters cooperating on a defined task, 18 object manipulation, and grasp analysis. To this end, we proposed a novel design for Althea II and enhanced the GUI by both development of a strong tip-detection algorithm and adding more operational functions.…”

Section: Introductionmentioning

confidence: 99%

Design and experimental evaluation of an automated catheter operating system

et al. 2021

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Manual catheter‐based interventions (CBIs) suffer from exposure of the interventionalists to X‐ray, and dependence of their performance on the expertise and fatigue level of the interventionalists. Robot‐assisted catheterization systems (RACS) have been introduced in recent years to improve the efficiency of CBIs; however, using them is still associated with some difficulties such as set‐up dependency to a specific type of intervention instrument, not being portable, and offering limited options of operation modes. The objective of this research is to develop a new RACS to address these shortcomings. We propose Althea II as an improvement for our previously introduced RACS, Althea I. Althea II is designed for both research purposes and clinical applications including catheter‐based cardiovascular interventions. Althea II benefits from a novel structural design leading to a significantly reduced weight and making the device inclusive for a broader range of intervention instruments. Also, a tip detection algorithm is developed and integrated into the graphical user interface (GUI) to enable image‐based navigation, and accordingly, fully automatic navigation. Althea II has improved the outcome of catheter‐based interventions by increased accuracy and precision of the intervention. The system can navigate the catheter tip to a designated target with an accuracy higher than 90% in both velocity and positioning mode. The device is associated with an upgraded GUI equipped with a strong tip detection algorithm with an accuracy of 0.05 mm. Moreover, Althea II gains from a quicker assembly time (20 minutes, which equals five times faster). The independency from specific catheters, several modes of function, an imaged‐based feedback control, portability, and a remote function should allow operation even from beginners and reduce X‐ray exposure. The preliminary research studies verified the accuracy and repeatability of Althea II, demonstrated the feasibility and applicability of using the set‐up in multiple applications, and highlighted the improved set‐up capabilities over the currently available RACS.

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Dynamic modeling and system identification of internally actuated, small-sized continuum robots

Cited by 19 publications

References 26 publications

How to Model Tendon-Driven Continuum Robots and Benchmark Modelling Performance

How to Model Tendon-Driven Continuum Robots and Benchmark Modelling Performance

A workload identification method of industrial robot combining dynamic model and convolutional neural network

Design and experimental evaluation of an automated catheter operating system

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