Intelligent spraying robot for building walls with mobility and perception

Wang, Yue; Xie, Liangxi; Wang, Hao; Zeng, Wenhao; Ding, Youming; Hu, Tang; Tang, Zheng; Liao, Hongyu; Hu, Jing

doi:10.1016/j.autcon.2022.104270

Cited by 23 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, the position and orientation matrix of the TBM robot end nozzle motion path point calculated in Cartesian coordinates is shown below. (10) Left multiply the left and right sides of equation ( 10) by the inverse matrix of to obtain: (11) Record , is expanded as:…”

Section: Solution Of θ4mentioning

confidence: 99%

“…Substituting the position and orientation equation (10) of the end nozzle of the intelligent shotcrete robot calculated by forward kinematics in the base coordinate system into the calculation, we obtain the three-dimensional cloud map of the robot arm. After conducting workspace analysis, we obtained the results shown in Figure 5.…”

Section: Workpace Analysismentioning

confidence: 99%

“…In addition, this method has problems such as large rebound, harsh construction environment, and high labor cost. The research in this article aims to solve this series of problems 10 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Kinematics and spatial structure analysis of TBM gunite robot based on D-H parameter method

Jia,

Liu,

Cao

et al. 2024

Preprint

View full text Add to dashboard Cite

In modern tunnel construction, TBM (Tunnel Boring Machine) plays a very important role. In response to the needs of tunnel wall reinforcement and TBM automated construction for tunnel construction, a shotcrete mechanism mounted on the TBM is designed. In order to evaluate the kinematic performance of the mechanism, this paper studies the forward and inverse kinematics and spatial architecture of the TBM shotcrete robot. Firstly, based on the D-H parameter method, the number of joints and links is determined and structural analysis is performed to obtain the robot's forward kinematics equation, achieving the mapping between joint space and pose space. Then, by determining the joint variables, the mapping of the end tool in Cartesian space is achieved. Finally, based on the Monte Carlo random sampling method, the workspace of the robot is constructed, and its reachability and flexibility within the robot workspace are evaluated. The performance of the device is verified by building a prototype, which meets the requirements well. Through the research in this paper, it can provide theoretical basis and guidance for the design and control of the shotcrete robot.

show abstract

Section: Solution Of θ4mentioning

confidence: 99%

Section: Workpace Analysismentioning

confidence: 99%

See 1 more Smart Citation

Kinematics and spatial structure analysis of TBM gunite robot based on D-H parameter method

Jia,

Liu,

Cao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Li et al [19] designed a magnetic-adsorption-type robot to replace workers in spraying operations. Wang et al [20] applied a parallel-adjustment algorithm to an intelligent spraying robot for building walls. Yuping et al [21] designed a wall-climbing robot for hull-plate spraying in the dock (WCR-HPSD).…”

Section: Introductionmentioning

confidence: 99%

Optimization Design of a Recovery System for an Automatic Spray Robot and the Simulation of VOC Recovery

Huang,

Liu,

et al. 2024

JMSE

View full text Add to dashboard Cite

A recovery system for an automatic spraying robot to conduct the spraying operation outdoors for ships is designed in this paper, which addresses the pollution problem of volatile organic compounds (VOCs) by employing the vacuum recovery method. The recovery system consists of the recovery hood, nozzle, and vacuum tubes. The recovery hood is the critical part of the recovery system and is designed with internal and external cavities, as well as four vacuum tubes for recycling VOCs. Based on the computational fluid dynamics (CFD) method, simulation in the time domain of the gas–liquid interaction, droplet evaporation, and wall impingement is conducted. To identify the better recovery performance, three vacuum recovery-hood schemes are designed, and their performance is compared. The numerical results show that the distance between the vacuum tubes and the intake gap has a significant impact on the VOCs’ recovery effect. One of the main reasons for the escape of VOCs is that the swirling airflows in the baffle plane act as vortices which may capture VOCs, causing the accumulation of VOCs beyond the capacity of the external cavity. Dividing the external cavity into four chambers with deflectors (with each chamber equipped with one vacuum tube only) can significantly reduce the leakage rate of the recovery system. The recovery system provides a theoretical solution for implementing the prevention and control of VOCs in shipyards as soon as possible.

show abstract

“…Parallel robots, in comparison with their serial counterpart, offer intrinsic advantages of high precision, high stiffness and high loading capability [1], therefore, they have been extensively applied and brought breakthroughs in numerous fields such as the motion generator [2], parallel machine tools [3–5], antenna mount [6], sensors [7], manufacturing [8, 9] and rehabilitation exercises [10–12]. The motion‐planning and control problem play crucial roles in the field of robotics regardless of serial robots or parallel robots [13–15].…”

Section: Introductionmentioning

confidence: 99%

Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

Jun-hai

Zhang

2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

The forward kinematics of parallel robots is a challenging issue due to its highly coupled non‐linear relation among branch chains. This paper presents a novel approach to forward kinematics of parallel robots based on kernel extreme learning machine (KELM). To tackle with the forward kinematics solution of fully parallel robots, the forward kinematics solution of parallel robots is equivalently transformed into a machine learning model first. On this basis, a computational model combining sparrow search algorithm and KELM is then established, which can serve as both regression and classification. Based on SSA‐optimised KELM (SSA‐KELM) established in this study, a binary discriminator for judging the existence of the forward kinematics solution and a multi‐label regression model for predicting the forward kinematics solution are built to obtain the forward kinematics general solution of parallel robots with different structural configurations and parameters. To evaluate the proposed model, a numerical case on this dataset collected by the inverse kinematics model of a typical 6‐DOF parallel robot is conducted, followed by the results manifesting that the binary discriminator with the discriminant accuracy of 88.50% is superior over ELM, KELM, support vector machine and logistic regression. The multi‐label regression model, with the root mean squared error of 0.06 mm for the position and 0.15° for the orientation, outperforms the double‐hidden‐layer back propagation (2‐BP), ELM, KELM and genetic algorithm‐optimised KELM. Furthermore, numerical cases of parallel robots with different structural configurations and parameters are compared with state‐of‐the‐art models. Moreover, these results of numerical simulation and experiment on the host computer demonstrate that the proposed model displays its high precision, high robustness and rapid convergence, which provides a candidate for the forward kinematics of parallel robots.

show abstract

Intelligent spraying robot for building walls with mobility and perception

Cited by 23 publications

References 24 publications

Kinematics and spatial structure analysis of TBM gunite robot based on D-H parameter method

Kinematics and spatial structure analysis of TBM gunite robot based on D-H parameter method

Optimization Design of a Recovery System for an Automatic Spray Robot and the Simulation of VOC Recovery

Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

Contact Info

Product

Resources

About