Design and Validation of a Self-Driven Joint Model for Articulated Arm Coordinate Measuring Machines

Hu, Yi; Huang, Wei; Hu, Penghao; Liu, Wen; Ye, Bing

doi:10.3390/app9153151

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…Li et al [7] designed a six-degree-of-freedom AACMM, which uses a built-in balance mechanism to reduce the operator's labor intensity. Hu et al [8,9] proposed a new type of self-driving joint module, which uses a brushless motor, a harmonic reducer and a precision shaft system to complete the design of a self-driving single-joint module. Rugbani Ali et al [10] constructed a miniature coordinate measuring machine to reduce measurement errors by analyzing the structure's geometric parameters.…”

Section: Introductionmentioning

confidence: 99%

Structural design and performance analysis of a self-driven articulated arm coordinate measuring machine

Yang¹,

Mei²,

Zhang³

et al. 2021

Meas. Sci. Technol.

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To solve the problem that the traditional articulated arm coordinate measuring machine cannot measure automatically, a self-driven articulated arm coordinate measuring machine (AACMM) is proposed. The length of the connecting rods of the AACMM was allocated according to the design indicators. The AACMM virtual prototype was assembled based on the joint module selection and joint component design, and its measurement space range was also verified. The AACMM ideal measurement model was established based on MDH methodology. The static deformation of the structure and the influence of the dynamic flexible deformation on the positioning error of the probe of the measuring machine was analyzed, respectively. The results show that the measurement space range of the AACMM designed in this paper can meet the design index of the measuring radius. The probe position error caused by static deformation of the measuring machine after structural optimization was reduced by an order of magnitude. The positioning error of the probe caused by the dynamic deformation of the AACMM structure meets the positioning accuracy index. In the constant-speed touch measurement stage, the instantaneous position error of the probe changes linearly with time, and the optimal touch speed (6.6 mm/s, 6.4 mm/s) exists to minimize the probe positioning error. During the variable-speed approach stage, the influence of angular acceleration and velocity of each joint on the positioning error of the probe can be negligible when AACMM in the typical posture. In the extreme posture, , the inertial force of the measuring machine structure and the instantaneous position error of the probe are the smallest with the optimal joint angular acceleration ( ) and angular velocity ( ). The structural design and positioning error performance analysis of self-driving AACMM can provide a theoretical research foundation for subsequent trajectory planning and error compensation modeling.

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Section: Introductionmentioning

confidence: 99%

Structural design and performance analysis of a self-driven articulated arm coordinate measuring machine

Yang¹,

Mei²,

Zhang³

et al. 2021

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…En la mayoría de los casos no se requeriría sistema de suspensión, pero no se puede evitar, ya que el sistema de suspensión Rocker-Bogie permite al robot superar obstáculos (Kim et al, 2012). En lo viable, se trata de diseñar una geometría sencilla, reduciendo el coste computacional en la ejecución de la simulación (Cabrera et al, 2021) al incorporar, no solo la geometría del prototipo, sino datos de entrada o contorno con el fin de anticiparse al proceso real, validarlo y, de ser necesario, rediseñarlo para obtener la mejor configuración (Hu et al, 2019).…”

Section: Figuraunclassified

Diseño de un prototipo de robot con geometría Rocker-Bogie

Montaleza¹,

Mayorga²,

Gallegos³

et al. 2022

Enfoque UTE

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En la presente investigación se diseña y valida una geometría de suspensión tipo Rocker-Bogie basados en el análisis de grados de libertad para su futura implementación dentro del sector de la industria de exploración espacial. Ello tiene como finalidad principal la recolección, transporte de basura y desperdicios a sectores estratégicos dentro de las instalaciones, sorteando obstáculos de diferente nivel, aunque también podría aplicarse en el área industrial, agrícola y minera, en las que se requiera un sistema robusto y estable. Se parte estableciendo un factor de seguridad determinado a través de coeficientes que involucren seguridad y economía. Luego, se realiza el cálculo de las dimensiones y fuerzas a soportar por los elementos estructurales conocidos como Rocker y Bogie que son los encargados de transmitir el soporte al chasis, además de brindar soporte a las conexiones para los componentes de los ejes y motores para las ruedas. Del diseño y cálculo de estos elementos depende que el prototipo de robot sea capaz de movilizarse a través de campos irregulares sorteando obstáculos como: anomalías en la superficie, baches, rocas y bordillos. Para la validación del diseño del prototipo se realiza un análisis estático de los elementos estructurales (Rocker y Bogie) por medio del parámetro de Von Misses, obteniendo un nivel de seguridad elevado antes de la ruptura. Posteriormente, se ejecuta la simulación del prototipo en base a una pista creada en el software analizando parámetros de velocidad, aceleración y desplazamientos en los ejes X y Y. Finalmente, se comparan los resultados obtenidos de la simulación basándose, principalmente, en altura y longitud máxima entre eje frontal y posterior, siendo crucial el ángulo máximo de inclinación permisible.

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“…Brahmi et al [ 33 ] proposed a solution based on human upper limb inverse kinematics and achieved robust nonlinear control for a 7-DOF manipulator. Yi et al [ 34 ] proposed a self-driven joint model that combines articulated arm coordinate measuring machine joints with mechanical arms to achieve automatic rotation. Robotic arms are also used in powered exoskeleton and brain and cognitive neuroscience research.…”

Section: Related Workmentioning

confidence: 99%

A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control

Liu

Gao

et al. 2020

Sensors

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Humanoid robots are equipped with humanoid arms to make them more acceptable to the general public. Humanoid robots are a great challenge in robotics. The concept of digital twin technology complies with the guiding ideology of not only Industry 4.0, but also Made in China 2025. This paper proposes a scheme that combines deep reinforcement learning (DRL) with digital twin technology for controlling humanoid robot arms. For rapid and stable motion planning for humanoid robots, multitasking-oriented training using the twin synchro-control (TSC) scheme with DRL is proposed. For switching between tasks, the robot arm training must be quick and diverse. In this work, an approach for obtaining a priori knowledge as input to DRL is developed and verified using simulations. Two simple examples are developed in a simulation environment. We developed a data acquisition system to generate angle data efficiently and automatically. These data are used to improve the reward function of the deep deterministic policy gradient (DDPG) and quickly train the robot for a task. The approach is applied to a model of the humanoid robot BHR-6, a humanoid robot with multiple-motion mode and a sophisticated mechanical structure. Using the policies trained in the simulations, the humanoid robot can perform tasks that are not possible to train with existing methods. The training is fast and allows the robot to perform multiple tasks. Our approach utilizes human joint angle data collected by the data acquisition system to solve the problem of a sparse reward in DRL for two simple tasks. A comparison with simulation results for controllers trained using the vanilla DDPG show that the designed controller developed using the DDPG with the TSC scheme have great advantages in terms of learning stability and convergence speed.

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Design and Validation of a Self-Driven Joint Model for Articulated Arm Coordinate Measuring Machines

Cited by 8 publications

References 25 publications

Structural design and performance analysis of a self-driven articulated arm coordinate measuring machine

Structural design and performance analysis of a self-driven articulated arm coordinate measuring machine

Diseño de un prototipo de robot con geometría Rocker-Bogie

A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control

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