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Intelligent Sensing and Control of Road Construction Robot Scenes Based on Road Construction

Zhongping Chen, Weigong Zhang*

School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

* Corresponding Author: Weigong Zhang. Email: email

Structural Durability & Health Monitoring 2024, 18(2), 111-124. https://doi.org/10.32604/sdhm.2023.043563

Abstract

Automatic control technology is the basis of road robot improvement, according to the characteristics of construction equipment and functions, the research will be input type perception from positioning acquisition, real-world monitoring, the process will use RTK-GNSS positional perception technology, by projecting the left side of the earth from Gauss-Krueger projection method, and then carry out the Cartesian conversion based on the characteristics of drawing; steering control system is the core of the electric drive unmanned module, on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles, the steering system key components such as direction, torque sensor, drive motor and other models are established, the joint simulation model of unmanned engineering vehicles is established, the steering controller is designed using the PID method, the simulation results show that the control method can meet the construction path demand for automatic steering. The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm, and never realize the construction-based path planning, and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm. With the collaboration of various modules, the automatic construction simulation results of this robot show that the design path and control method is effective.

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Cite This Article

Chen, Z., Zhang, W. (2024). Intelligent Sensing and Control of Road Construction Robot Scenes Based on Road Construction. Structural Durability & Health Monitoring, 18(2), 111–124. https://doi.org/10.32604/sdhm.2023.043563



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