基于数字孪生的移动机器人虚实定位补偿研究

Compensation of Virtual and Real Positioning for Mobile Robots Based on Digital Twins

随着移动机器人应用的不断拓展,精确定位和定向已成为实现自主导航与控制的关键技术。然而,由于环境复杂性和传感器误差等因素的影响,移动机器人的位姿估计会存在一定偏差。为此,以ROS系统为基础搭建了移动机器人数字孪生系统,并在该系统下提出了一种虚实定位误差补偿方法。针对虚实映射过程中存在的定位误差,设计了一种基于自适应遗传算法的预测优化控制器(AGA-POC),以发布控制指令的方式驱动被控对象实现对定位误差的补偿。实验结果表明,相较于MPC补偿,移动机器人的位姿误差和运行轨迹偏差在所提的补偿方法下分别降低了60.43%和35.66%,轨迹相似度和控制器的响应速率分别提升了69.28%和71.14%,先后从位姿精度、轨迹准确度、控制器响应速率3个方面验证了该补偿方法的可行性与有效性。

With the expanding applications of mobile robots precise positioning and orientation have become key techniques for achieving autonomous navigation and control.However due to the complexity of the environment and sensor errors and other factors there are certain biases in the estimation of the posture of mobile robots.For this reason a digital twin system is built for mobile robots based on the ROS and a virtual-real positioning error compensation method is proposed under this system.An Adaptive Genetic Algorithm-based Predictive Optimization Controller(AGA-POC)is designed to drive the controlled models by issuing control commands to compensate for the positioning error existed in the virtual-real mapping process.The experimental results show that compared with Model Predictive Control(MPC)compensation the positioning error and trajectory deviation of the mobile robots are reduced by 60.43%and 35.66%respectively the trajectory similarity and controller response rate are improved by 69.28%and 71.14%respectively which verifies the feasibility and validity of the compensation strategy in terms of positioning accuracy trajectory accuracy and controller response rate.