系统仿真学报 ›› 2025, Vol. 37 ›› Issue (6): 1531-1541.doi: 10.16182/j.issn1004731x.joss.24-0516

• 论文 • 上一篇    

基于视觉动捕的仿真掘进机操作系统

李永玲1,3,4, 刘凌志1,3, 周百顺2, 雷经发1,3,4, 张淼1,3, 赵汝海1,3   

  1. 1.安徽建筑大学 机械与电气工程学院,安徽 合肥 230601
    2.中国劳动关系学院 计算机学院,北京 100048
    3.工程机械智能制造安徽省教育厅重点实验室,安徽 合肥 230601
    4.过程装备与控制工程四川省高校重点实验室,四川 自贡 643000
  • 收稿日期:2024-05-15 修回日期:2024-07-24 出版日期:2025-06-20 发布日期:2025-06-18
  • 通讯作者: 周百顺
  • 第一作者简介:李永玲(1985-),女,讲师,博士,研究方向为机器视觉。
  • 基金资助:
    安徽省重点研究与开发计划(1804a09020009);安徽省高校自然科学研究重大项目与重点项目(J2021ZA0068);安徽省高校协同创新项目(GXXT-2022-019);过程装备与控制工程四川省高校重点实验室开放基金(GK202101)

Operation System for Simulation Roadheader Based on Visual Motion Capture

Li Yongling1,3,4, Liu Lingzhi1,3, Zhou Baishun2, Lei Jingfa1,3,4, Zhang Miao1,3, Zhao Ruhai1,3   

  1. 1.School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 236001, China
    2.School of Computer Science, China University of Labor Relations, Beijing 100048, China
    3.Key Laboratory of Intelligent Manufacturing of Construction Machinery, Anhui Education Department, Hefei 230601, China
    4.Sichuan Provincial Key Laboratory of Process Equipment and Control Engineering, Zigong 643000, China
  • Received:2024-05-15 Revised:2024-07-24 Online:2025-06-20 Published:2025-06-18
  • Contact: Zhou Baishun

摘要:

为实现仿真掘进环境中的人机自然交互,设计了一种基于视觉动捕的仿真掘进机操作系统。视觉动捕单元基于Mediapipe框架,通过相机捕捉手势动作,实现真实世界与虚拟空间的映射;针对手部关键点数据在大范围运动中的不合理跳变问题,通过设置加权质心提出一种改进的卡尔曼滤波算法,并依据操作者手势输出对应指令。实验结果表明:该方法在均方误差、信噪比、近似熵参数上更具有优势,所需的特定手势识别正确率可达92%以上,操作者可实现对仿真掘进机的高效控制。

关键词: 人机交互, 手势捕捉, 卡尔曼滤波, 仿真操作系统, 近似熵

Abstract:

To enhance the natural human-machine interaction in simulation roadheader environment, a vision-based simulation roadheader operation system is proposed. The visual motion capture unit is based on the MediaPipe framework, which captures hand gestures through cameras and creates a correspondence between the physical world and virtual space. An improved Kalman filter algorithm is proposed by setting a weighted centroid to address the issue of unreasonable jumps in hand keypoint data during large-scale movements. The operator's gestures are discerned and the corresponding commands are conveyed. The results show that the improved method has significant advantages over the control group in terms of mean square error, signal-to-noise ratio, and approximate entropy parameters. The gesture recognition system is developed with an accuracy rate exceeding 92%. This interface enables the operator to efficiently control the simulated tunneling machine.

Key words: human-machine interaction, motion capture, Kalman filter, simulated operation system, approximate entropy

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