面向移动作业的腿足机器人数字孪生系统

Digital twin system of legged robot for mobile operation

针对移动作业中的腿足机器人,结合数字孪生技术,设计整机系统的体系结构、模块结构、硬件框架和软件框架,通过集成多个传感器输入和数据源,可在移动场景下获得可靠准确的环境状态和机器人状态.基于点线特征匹配理论,优化腿足机器人的自主定位精度和鲁棒性,结合环境建模数据实现高效里程计和实时移动建图.提出建立数字孪生模型的通用方法,并通过误差补偿保证机器人数字孪生体的运动状态与实际机器人的运动状态高度一致.在数据集和真实机器人上进行实验,结果表明所提出的数字孪生系统不仅能够在不同的腿足机器人平台上稳定高效运行,而且能够保证实时状态反馈和里程计定位精度.与ORB-SLAM3相比,内存开销降低约68.7%,CPU使用率降低约17.8%.硬件实验表明,通信延迟与网络延迟基本一致,约为30 ms,有助于提高任务执行效率.

A digital twin system for the mobile operation of legged robots was proposed,encompassing the design of architecture,module structure,hardware framework,and software framework.The system enabled reliable and accurate acquisition of environmental states and robot states in mobile scenarios by integrating multiple sensor inputs and data sources.The point and line feature matching theory was used to optimize the autonomous positioning accuracy and the robustness of the legged robot,and the odometer functionality and the real-time mobile mapping were effectively achieved through integration with the environmental modeling data.A general modeling method was introduced to establish a digital twin model that ensured high consistency between the simulated robot motion state and the real robot motion state through error compensation techniques.Experimental results on both datasets and real robots demonstrated that the proposed digital twin system not only operated stably and efficiently across various legged robot platforms but also ensured the real-time state feedback and the odometer positioning accuracy.Compared with ORB-SLAM3,the memory overhead was reduced by about 68.7%,and the CPU usage was reduced by about 17.8%.The hardware experiments showed that the communication delay was basically consistent with the network delay of about 30 ms,which helped to improve the efficiency of task execution.