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基于物联网目标图像增强技术和反向传播神经网络的非完整移动机器人轨迹控制分析

The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network.

作者信息

Zhao Lanfei, Wang Ganlin, Fan Xiaosong, Li Yufei

机构信息

The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China.

Huabei Oil Communication Co., Ltd., Cangzhou, China.

出版信息

Front Neurorobot. 2021 Mar 22;15:634340. doi: 10.3389/fnbot.2021.634340. eCollection 2021.

DOI:10.3389/fnbot.2021.634340
PMID:33828475
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8020999/
Abstract

The trajectory tracking and control of incomplete mobile robots are explored to improve the accuracy of the trajectory tracking of the robot controller. First, the mathematical kinematics model of the non-holonomic mobile robot is studied. Then, the improved Backpropagation Neural Network (BPNN) is applied to the robot controller. On this basis, a mobile robot trajectory tracking controller combining the fuzzy algorithm and the neural network is designed to control the linear velocity and angular velocity of the mobile robot. Finally, the robot target image can be analyzed effectively based on the Internet of Things (IoT) image enhancement technology. In the MATLAB environment, the performances of traditional BPNN and improved BPNN in mobile robots' trajectory tracking are compared. The tracking accuracy before and after the improvement shows no apparent differences; however, the training speed of improved BPNN is significantly accelerated. The fuzzy-BPNN controller presents significant improvements in tracking speed and tracking accuracy compared with the improved BPNN. The trajectory tracking controller of the mobile robot is designed and improved based on the fuzzy BPNN. The designed controller combining the fuzzy algorithm and the improved BPNN can provide higher accuracy and tracking efficiency for the trajectory tracking and control of the non-holonomic mobile robots.

摘要

为提高机器人控制器轨迹跟踪的精度，对非完整移动机器人的轨迹跟踪与控制进行了探索。首先，研究了非完整移动机器人的数学运动学模型。然后，将改进的反向传播神经网络（BPNN）应用于机器人控制器。在此基础上，设计了一种结合模糊算法和神经网络的移动机器人轨迹跟踪控制器，以控制移动机器人的线速度和角速度。最后，基于物联网（IoT）图像增强技术，可有效分析机器人目标图像。在MATLAB环境中，比较了传统BPNN和改进BPNN在移动机器人轨迹跟踪中的性能。改进前后的跟踪精度无明显差异；然而，改进BPNN的训练速度显著加快。与改进BPNN相比，模糊-BPNN控制器在跟踪速度和跟踪精度方面有显著提高。基于模糊BPNN对移动机器人的轨迹跟踪控制器进行了设计与改进。所设计的结合模糊算法和改进BPNN的控制器可为非完整移动机器人的轨迹跟踪与控制提供更高的精度和跟踪效率。

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