A Marker-Free 2D Image-Guided Method for Robot-Assisted Fracture Reduction Surgery

Zhu, Shijie; Zhao, Zhe; Chen, Yu; Deng, Jiuzheng; Zhang, Bicong; Zhu, Jianjin; Chen, Yitong; He, Dalin; Pan, Yuxin; Zheng, Gangtie

doi:10.1007/s10846-021-01453-8

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…MðqÞ ∈ R 6�6 is the symmetric positive-definite inertia matrix, C q; _ q ð Þ ∈ R 6�6 is the Centrifugal and Coriolis force matrix, and G ∈ R 6 is gravity matrix. 6 represents the interaction force between the robot's end-effector and muscles during the reduction process in joint space, where J is the Jacobian matrix, and F e is obtained from the six-axis force sensor. τ dis ∈ R 6 represents the unknown disturbance force, and τ ∈ R 6 represents the applied joint torque.…”

Section: Dynamic Model Of the Fracture Reduction Robotmentioning

confidence: 99%

“…With the advancement of robotic technology, robot-assisted surgery has emerged as a potential solution, offering the potential to improve patient outcomes. 5 Currently, the control methods for fracture reduction robots include Host Computer Jog Control (HCJC), 6,7 master-slave control, 8,9 and automatic control. 10,11 Recent research in this field is partially summarised in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the control methods for fracture reduction robots include Host Computer Jog Control (HCJC), 6,7 master‐slave control, 8,9 and automatic control 10,11 . Recent research in this field is partially summarised in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network

Lei,

Wang

2024

Robotics Computer Surgery

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BackgroundFor the fracture reduction robot, the position tracking accuracy and compliance are affected by dynamic loads from muscle stretching, uncertainties in robot dynamics models, and various internal and external disturbances.MethodsA control method that integrates a Radial Basis Function Neural Network (RBFNN) with Nonlinear Disturbance Observer is proposed to enhance position tracking accuracy. Additionally, an admittance control is employed for force tracking to enhance the robot's compliance, thereby improving the safety.ResultsExperiments are conducted on a long bone fracture model with simulated muscle forces and the results demonstrate that the position tracking error is less than ±0.2 mm, the angular displacement error is less than ±0.3°, and the maximum force tracking error is 26.28 N. This result can meet surgery requirements.ConclusionsThe control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance.

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Section: Dynamic Model Of the Fracture Reduction Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network

Lei,

Wang

2024

Robotics Computer Surgery

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“…In today's world, robotics engineering has become an essential component of many sectors of applied engineering and industrial applications. Its applications include the oil and gas industry (Shukla & Karki, 2016), agriculture (Mahmud et al, 2020), and medicine (Zhu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization-based reduced-order controller for balancing control issues with a two-wheeled mobile robot

Duddeti¹

2023

Preprint

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Existing controllers for a two-wheel mobile robot (TWMR) are based on higher-order controller (HOC) structures, which are complex and challenging to analyze, synthesize, and implement on hardware. To address this issue, the authors propose a reduced-order controller (ROC) for the pre-specified HOC that can effectively handle unpredictable dynamics. The proposed approach involves two phases. In phase 1, the ROC is made using dominant poles and moment matching. In phase 2, the particle swarm optimization (PSO) method improves the ROC numerator coefficients by minimizing the root mean square error and integral square error between the step responses and Bode magnitude plots of the HOC and ROC. One benefit of this technique is that the PSO algorithm’s search space bounds are not entirely arbitrary. They are instead picked based on the numerator coefficients calculated in Phase 1. It overcomes issues with evolutionary algorithms, such as random search space selection, optimization of additional decision variables, and longer simulation duration. Comparisons are made between the performance of the proposed controller and controllers built using other approaches. The determinations reveal that the proposed ROC outperforms these current techniques, resulting in a more straightforward and effective solution for regulating the unstable TWMR system. The efficacy of higher- and lower-order controllers is evaluated using a perturbed two-wheeled mobile robot. This evaluation focuses on analyzing various time response parameters and performance indices, including integral time absolute error (ITAE), integral square error (ISE), and integral absolute error (IAE). The MATLAB environment is employed as the preferred tool to carry out these simulations.

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A Novel Rank-order-centroid Based Reduction of Self-balanced-bicycle-robot Controller Using Grey-wolf Optimizer

Yadav

Meena

Singh

2022

J Intell Robot Syst

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A Marker-Free 2D Image-Guided Method for Robot-Assisted Fracture Reduction Surgery

Cited by 4 publications

References 33 publications

Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network

Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network

Particle swarm optimization-based reduced-order controller for balancing control issues with a two-wheeled mobile robot

A Novel Rank-order-centroid Based Reduction of Self-balanced-bicycle-robot Controller Using Grey-wolf Optimizer

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