Li Renjun, Shang Xiaoyu, Wang Yang, Liu Chunbai, Song Linsen, Zhang Yiwen, Gu Lidong, Zhang Xinming
School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Changchun Guanghua Microelectronic Equipment Engineering Center Co., Ltd., Changchun 130012, China.
Sensors (Basel). 2024 Dec 19;24(24):8101. doi: 10.3390/s24248101.
Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces. By a virtual engine and digital twins, this study put forward a visualization monitoring and control system framework which can address the difficulties in the intelligent factories while managing a variety of data sources, such as virtual-real integration, real-time feedback, and other issues. To develop a more realistic dynamic model for the robots, we presented a neural-network-based compensation technique for the nonlinear dynamic model parameters of outdoor mobile robots. A physical prototype was applied in the experiments, and the results showed that the system is capable of controlling and monitoring outdoor mobile robots online with good visualization effects and high real-time performance. By boosting the positional accuracy of robots by 18% when navigating obstacles, the proposed precise kinematic model can increase the inspection efficiency of robots. The visualization monitoring and control system enables visual, digital, multi-method, and complete real-time inspections in high-risk factories, such as new energy battery factories, to ensure the safe and stable operations.
巡检机器人在提高危险识别能力和加强安全管理方面发挥着重要作用，在配电、石化和新能源电池厂等许多领域的高危环境检查中至关重要。目前，机器人的位置精度是其广泛应用的主要障碍。需要精确的运动学模型和机器人控制系统，以提高其在非结构化表面上移动时的定位精度。通过虚拟引擎和数字孪生技术，本研究提出了一种可视化监控系统框架，该框架能够解决智能工厂中的难题，同时管理各种数据源，如虚实集成、实时反馈等问题。为了开发更逼真的机器人动态模型，我们提出了一种基于神经网络的户外移动机器人非线性动态模型参数补偿技术。在实验中应用了物理原型，结果表明该系统能够在线控制和监控户外移动机器人，具有良好的可视化效果和高实时性能。通过在避障时将机器人的位置精度提高18%，所提出的精确运动学模型可以提高机器人的巡检效率。可视化监控系统能够在新能源电池厂等高风险工厂中实现可视化、数字化、多方法和完整的实时检查，以确保安全稳定运行。
