Journal of Textile Research ›› 2023, Vol. 44 ›› Issue (12): 170-180.doi: 10.13475/j.fzxb.20220606701

• Machinery & Accessories • Previous Articles     Next Articles

Mobile robot positioning method in textile production environments

LI Xun1,2(), LI Zhewen1, ZHANG Tingwen1, JING Junfeng1,2, LI Pengfei1   

  1. 1. School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
    2. Xi'an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi'an, Shaanxi 710600, China
  • Received:2022-12-28 Revised:2023-02-05 Online:2023-12-15 Published:2024-01-22


Objective The intellectualization and green production of textile industry are the contents that must be upgraded following the "double carbon" strategy, and the large number of applications of mobile robots will be a fact in the future. However, robot has not been widely used in the textile workshop, and most of the robot positioning method are based on the traditional magnetic signals such as conductor. With the expanding of the scale of production, scalability is not strong. The ground environment, textile machinery and textile workshop electromagnetic interference problem will have to be solved. Therefore, a multi-sensor hybrid filtering method is proposed.
Method In order to solve the impact of physical environment in textile workshop, the Inertial Measurement Unit (IMU) sensor data carried by the robot was compared with other sensor data, and a data preprocessing model was established to reduce the impact of ground and electromagnetic on sensor data. According to the characteristics of each sensor, the data processed by LiDAR, IMU and wheel odometer were fused by UKF(Unscented Kalman Filter) filter, and the global pose estimated by AMCL (Adaptive Mentcarto Localization)meter was added to UKF to solve the problem of positioning failure caused by the initial value and abnormal data.
Results The simulation environment of textile workshop with and without electromagnetic interference was constructed based on ROS operating system, and the algorithm proposed in this paper was compared with other algorithms. Under the condition of no electromagnetic interference, the positioning accuracy of single sensor was found to decrease significantly and even fail due to the influence of complex ground and similar environment in textile workshop (Fig. 6). Compared with UKF and EKF multi-sensor fusion algorithms, AMCL-UKF algorithm demonstrated better fitness and stronger robustness in the face of complex environment in textile workshop. AMCL-UKF algorithm reduced the positioning error by 85.9% and 34.8% and the maximum positioning error was reduced by 8.1% and 7.6%, indicating that multi-sensor fusion could effectively improve the positioning accuracy (Tab. 3). It showed that the multi-sensor algorithm could effectively reduce the error caused by EKF and UKF linearization process. In the environment with electromagnetic interference, as shown in Tab.4, the maximum error of the data preprocessing model was reduced by 32.5%, the average value reduced by 37.6%, the root means square error reduced by 36.7%, and the standard deviation reduced by 12.7%, proving the effectiveness of the method. The AMCL-UKF algorithm provided relatively accurate positioning accuracy and stability in the face of strong magnetic field interference in the textile workshop (Fig. 11).
Conclusion The experimental results show that the data preprocessing model can effectively reduce the influence of physical environment of textile workshop on sensor data. Through the AMCL algorithm, the global pose is calculated and input into the UKF filter, which can improve the positioning failure of the Kalman filter caused by the interference in the working process of the textile robot and improve the working stability. Problems such as electromagnetic interference, ground, and error introduction during collaborative work in textile workshop have limited influence on positioning accuracy. However, the proposed algorithm does not use data information interaction between multiple robots for collaborative positioning. The latest research method of robot positioning is graph-based optimization algorithm, which has higher positioning accuracy. Therefore, the future research direction is to improve the positioning accuracy through graph optimization algorithm by using mutual measurement between multiple robots and reduce the computational complexity of graph optimization algorithm to further improve the positioning accuracy of robots. In conclusion, the improved AMCL-UKF method in this paper can provide accurate positioning information for mobile robots and provide theoretical and experimental data support for mobile robot application schemes required for intelligent upgrading of textile workshops in the future.

Key words: textile automation, mobile robot, AMCL-UKF hybrid filtering, data fusion positioning

CLC Number: 

  • TP24

Fig. 1

Schematic diagram of space layout of common spinning machines in textile workshops. (a) Combing workshop; (b) Roving workshop; (c) Spinning workshop"

Fig. 2

Relationship between Cartesian world coordinate system and body coordinate system. (a) Schematic diagram of production environment space; (b) Three-dimensional coordinate system transformation;(c) Motion description of coordinate"

Fig. 3

Omnidirectional mobile robot of McNum wheel"

Fig. 4

Multi-sensor fusion positioning framework"

Tab. 1

Parameters of the dynamic weighted pretreatment model"

数据类型 传感器类型
轮式里程计 激光里程计 IMU
处理前线速度/(m·s-1) Vwheel Vradar Vimu
处理前角度/(rad·s-1) θwheel θradar θimu
处理后线速度/(m·s-1) VFW VFR
处理后角度/(rad·s-1) θFW θFR θFI
均值 Mwheel Mradar Mimu
线速度方差 δv,wheel δv,radar δv,imu
角度方差 δθ,wheel δθ,radar δθ,imu

Fig. 5

Textile workshop simulation environment. (a) Schematic diagram of drawing machine layout in combing shop; (b) Rviz visual map"

Tab. 2

Initializes data table"

数据类别 取值
初始位置/终止位置 (0,0,0)/(18.0,17.5,0)
移动机器人线速度/(m·s-1) 1
传感器干扰模拟 白噪声
电磁干扰幅度/(A·m-1) (1.0, 1.7)
受扰航向角偏差 (-11.81°, 11.81°)
并条机平面面积/m2 1×4

Fig. 6

Location track results of each algorithm in environment without electromagnetic interferenceLocation track results of each algorithm in environment without electromagnetic interference. (a) Each algorithm locates trajectory; (b) Wheel odometry; (c) Laser odometry; (d) EKF; (e) UKF; (f) AMCL-UKF"

Tab. 3

Evaluation index of positioning error of each algorithm in textile workshop environmentm"

算法 最大值 平均值 均方根误差 标准差
轮式里程计 1.298 0.589 0.701 0.380
激光里程计 0.279 0.160 0.169 0.055
EKF 0.198 0.135 0.140 0.037
UKF 0.197 0.133 0.138 0.037
AMCL-UKF 0.182 0.094 0.102 0.038

Fig. 7

Error diagram of each algorithm and real positioning trajectory. (a) Positioning error curve; (b) Histogram of positioning performance parameters; (c) Location error distribution map; (d) Box diagram of positioning error"

Fig. 8

Simulation curves of electromagnetic interference of motor A(a), motor B(b), motor C(c), motor D(d), motor F(e), motor G(f)"

Fig. 9

Electromagnetic interference curve after superposition"

Fig. 10

IMU heading angle data"

Tab. 4

Comparison of heading angle before and after pretreatmentrad"

类型 最大值 平均值 均方根误差 标准差
预处理前 10.80 8.15 8.24 1.26
预处理后 7.29 5.08 5.20 1.10

Fig. 11

Location track results of each algorithm in electromagnetic interference environment"

Tab. 5

Resource occupancy rate of each algorithm"

算法 CPU占用率/% 发布频率/Hz
EKF 19.4 100.2
UKF 20.2 100.2
图优化 28.0 82.59
AMCL-UKF 23.7 91.2
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