School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China.
CRRC Qingdao Sifang Vehicle Research Institute Co., Ltd, Qingdao, China.
Sci Rep. 2023 Jan 5;13(1):232. doi: 10.1038/s41598-023-27402-z.
As the mileage of subway is increasing rapidly, there is an urgent need for automatic subway tunnel inspection equipment to ensure the efficiency and frequency of daily tunnel inspection. The subway tunnel environment is complex, it cannot receive GPS and other satellite signals, a variety of positioning sensors cannot be used. Besides, there are random interference, wheel and rail idling and creep. All the above results in poor performance of conventional speed tracking and positioning methods. In this paper, a multi-sensor motion control system is proposed for the subway tunnel inspection robot. At the same time, a trapezoidal speed planning and a speed tracking algorithm based on MPC (Model Predictive Control) are proposed, which simplify longitudinal dynamics model to overcome the complex and variable nonlinear problems in the operation of the maintenance robot. The optimal function of speed, acceleration and jerk constraint is designed to make the tunnel inspection robot achieve efficient and stable speed control in the subway tunnel environment. In this paper, the "INS (inertial navigation system) + Odometer" positioning method is proposed. The difference between the displacement measured by the inertial navigation system and the displacement calculated by the odometer is taken as the measurement value, which reduces the dimension of the conventional algorithm. The closed-loop Kalman filter is used to establish the combined positioning model, and the system error can be corrected in real time with higher accuracy. The algorithms were verified on the test line. The displacement target was set to be 1 km and the limit speed was 60 km/h. The overshooting error of the speed tracking algorithm based on trapezoidal velocity planning and MPC was 0.89%, and the stability error was 0.32%. It improved the accuracy and stability of the speed following, and was much better than the PID speed tracking algorithm. At the speed of 40 km/h, the maximum positioning error of the robot within 2 km is 0.15%, and the average error is 0.08%. It is verified that the multi-sensor fusion positioning algorithm has significantly improved the accuracy compared with the single-odometer positioning algorithm, and can effectively make up for the position error caused by wheel-rail creep and sensor error.
随着地铁里程的快速增长，迫切需要自动地铁隧道检测设备来保证日常隧道检测的效率和频率。地铁隧道环境复杂，无法接收 GPS 等卫星信号，各种定位传感器无法使用。此外，还有随机干扰、轮轨空转和蠕动等问题。所有这些都导致传统速度跟踪和定位方法的性能不佳。本文为地铁隧道检测机器人提出了一种多传感器运动控制系统。同时，提出了一种梯形速度规划和基于 MPC（模型预测控制）的速度跟踪算法，该算法简化了纵向动力学模型，克服了维护机器人运行中复杂多变的非线性问题。设计了最优的速度、加速度和加加速度约束函数，使隧道检测机器人在地铁隧道环境中实现高效稳定的速度控制。本文提出了“惯性导航系统（INS）+里程计”定位方法。将惯性导航系统测量的位移与里程计计算的位移之间的差值作为测量值，降低了常规算法的维度。采用闭环卡尔曼滤波器建立组合定位模型，可以实时修正系统误差，具有更高的精度。在测试线上对算法进行了验证。将位移目标设置为 1km，限速为 60km/h。基于梯形速度规划和 MPC 的速度跟踪算法的超调误差为 0.89%，稳定误差为 0.32%。它提高了速度跟踪的准确性和稳定性，比 PID 速度跟踪算法要好得多。在 40km/h 的速度下，机器人在 2km 内的最大定位误差为 0.15%，平均误差为 0.08%。验证了多传感器融合定位算法与单里程计定位算法相比，显著提高了定位精度，可以有效弥补轮轨蠕动和传感器误差引起的位置误差。
