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Research on AGV positioning method combined with IMU and UWB

Qiu Jiandong, Zhang Yang, Tang Minan, Ma Panpan, Ran Jiajia

Archives of Transport

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2022

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Vol. 64, iss. 4

107--117

EN

Aiming at the problem that automated guided vehicle (AGV) is difficult to locate accurately due to the influence of environment and time drift when it works in the indoor intelligent storage system. In this paper, an extended Kalman filtering (EKF) framework is designed. In order to make full use of the original ranging values of ultra wideband (UWB) and inertial measurement unit (IMU), the framework realizes the fusion positioning between UWB module and IMU module in a tight coupling manner, so as to ensure that the system can still work when the available base station signal is inaccurate. Firstly, for the problem that the traditional UWB positioning method is easily affected by the non-line of sight (NLOS) error in-doors, the calculated positioning coordinate value is unstable. With the help of different NLOS probability distribution curves of different obstacles, the weighted least square method is applied to the UWB positioning method to determine the positioning coordinate value of UWB, which improves the sudden change of AGV positioning coordinate in the static environment. Then the data fusion algorithm is optimized, and the error value of IMU and UWB coordinate is taken as the observation value of EKF, which reduces the influence of cumulative error on IMU positioning results, provides the global optimal estimation of the system optimal state, and improves the fusion positioning accuracy. Finally, the measured data of UWB and IMU systems in indoor complex environment are simulated in MATLAB. The experimental results show that when NLOS signal seriously affects the positioning effect, the UWB and IMU combined positioning system can provide more reliable positioning results than the single IMU positioning system. It improves the positioning accuracy of AGV and provides a new idea for indoor positioning mode.

2

Determination of Truck Maintenance Allocation Scheme Based on SA-GA

Qiu Jiandong, Ren Wei, Tang Minan, Ma Panpan, Zhang Yang

Archives of Transport

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2022

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Vol. 62, iss. 2

59--71

EN

As an important department of railway transportation and production, large freight train depot is responsible for the regular overhaul and maintenance of railway trucks. The shunting operation of freight train depot covers the whole process of railway trucks entering, storing, overhauling and leaving the depot. It is an important step in the implementation of the maintenance operation. Usually, shunting personnel in the depot transport the trucks to be overhauled to the maintenance line by relying on the shunting operation plan, which is the key to determine the shunting operation plan according to the distribution relationship between vehicles and maintenance. Firstly, this paper analyzes the process of centralized shunting operation in the freight train depot and the factors affecting the time-consuming based on the research idea of flexible workshop scheduling problem. Then, on the premise that the proportion of the weight coefficient will have an impact on the time-consuming of truck busy and shunting in the shunting process, and with the goal of minimizing the time-consuming of truck maintenance busy and shunting, the allocation model between trucks and maintenance lines is established; In addition, an improved genetic algorithm is proposed to solve the established model; Finally, combined with the maintenance of railway trucks in a large freight train depot, an example analysis is carried out on this basis. The results demonstrate that using simulated annealing genetic algorithm to solve the model can obtain the allocation scheme between railway trucks and maintenance operation line. Under the influence of three different coefficients, compared with genetic algorithm, simulated annealing genetic algorithm can reduce the detention time of railway trucks in depot by 0.21%, 0.09% and 0.12% respectively, which is beneficial to reducing the detention time of maintenance vehicles in depot, It plays a positive role in improving the maintenance efficiency of trucks in the depot, and also provides new ideas for the research of railway truck shunting

3

Model predictive direct power control of energy storage quasi-Z-source grid-connected inverter

Tang Min'an, Yang Shangmei, Zhang Kaiyue, Wang Qianqian, Liu Chenggang, Dong Xuewang

Archives of Electrical Engineering

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2022

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Vol. 71, nr 1

21--35

EN

In order to overcome the shortcoming of large switching losses caused by variable switching frequency appears in the conventional finite control set model predictive control (FCS-MPC) algorithm, a model predictive direct power control (MP-DPC) for an energy storage quasi-Z-source inverter (ES-qZSI) is proposed. Firstly, the power prediction model of the ES-qZSI is established based on the instantaneous power theory. Then the average voltage vector in the 𝛼𝛽 coordinate system is optimized by the power cost function. Finally, the average voltage vector is used as the modulation signal, and the corresponding switching signal with fixed frequency is generated by the shoot-through segment space vector pulse width modulation (SVPWM) technology. The simulation results show that the ES-qZSI realizes six shoot-through actions per control cycle and achieves the constant frequency control of the system, which verifies the correctness of the proposed control strategy.

4

Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration

Tang Minan, Zhang Kaiyue, Wang Qianqian, Cheng Haipeng, Yang Shangmei, Du Hanxiao

Archives of Electrical Engineering

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2021

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Vol. 70, nr 2

307--323

EN

In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.