吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (4): 1496-1505.doi: 10.13229/j.cnki.jdxbgxb20200334

• 通信与控制工程 • 上一篇    

基于地形聚类分析的移动机器人速度自适应控制

刘明1,2(),荣学文1(),李贻斌1,张帅帅2,尹燕芳2,阮久宏3   

  1. 1.山东大学 控制科学与工程学院，济南 250061
    2.山东科技大学 电气与自动化学院，济南 250031
    3.山东交通学院 轨道交通学院，济南 250351
  • 收稿日期:2020-05-15 出版日期:2021-07-01 发布日期:2021-07-14
  • 通讯作者: 荣学文 E-mail:scrobotliuming@126.com;rongxw@sdu.edu.cn
  • 作者简介:刘明（1972-），男，副教授，博士研究生. 研究方向：机器人技术. E-mail：scrobotliuming@126.com
  • 基金资助:
    国家自然科学基金项目(U613223)

Speed adaptive control of mobile robot based on terrain clustering analysis

Ming LIU1,2(),Xue-wen RONG1(),Yi-bin LI1,Shuai-shuai ZHANG2,Yan-fang YIN2,Jiu-hong RUAN3   

  1. 1.College of Control Science and Engineering，Shandong University，Jinan 250061，China
    2.College of Electrical Engineering and Automation，Shandong University of Science and Technology，Jinan 250031，China
    3.School of Rail Transportation，Shandong Jiaotong University，Jinan 250351，China
  • Received:2020-05-15 Online:2021-07-01 Published:2021-07-14
  • Contact: Xue-wen RONG E-mail:scrobotliuming@126.com;rongxw@sdu.edu.cn

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摘要：

为实现移动机器人在不同地形环境中的速度自适应调整，提出一种利用机器人自身振动信息进行地形聚类分析并根据聚类结果进行运动速度自适应调整的方法。该方法基于机器人在运动过程中竖直方向的加速度信息及俯仰角信息，采用改进的高斯混合模型进行聚类分析，获得该地形相对于典型地形的隶属度，并结合地面的坡度起伏信息，通过模糊控制策略实现了机器人运动速度的自适应控制。为验证本文方法的正确性和实用性，利用Pioneer 3-AT四轮驱动全地形机器人平台进行了相关实验，实验结果表明，本文方法可使机器人准确地对不同地形进行聚类分析并实现了其在不同地形环境下的速度自适应调整，有效增强了机器人的地形适应性。

关键词: 自动控制, 地形, 聚类分析, 移动机器人, 速度自适应控制

Abstract:

In order to realize the self-adaptive adjustment of the motion speed of mobile robot in different terrain environments, a method of terrain clustering analysis based on robot vibration information and self-adaptive adjustment of motion speed based on clustering results is proposed. In this method, the acceleration information in the vertical direction and pitch angle information of the robot in the process of motion are used, and the terrain information is clustered based on the improved Gaussian mixture model to obtain the membership degree of the terrain relative to the typical terrain. In combination with the terrain undulation information, the adaptive control of the robot's motion speed is realized through the fuzzy control strategy. In order to verify the correctness and practicability of the proposed method, relevant experiments are carried out on the platform of pioneer 3-At four-wheel drive all terrain robot. The experimental results show that this method can make the robot cluster analysis on different terrain accurately and realize its speed adaptive adjustment under different terrain, which effectively enhances the terrain adaptability of the robot.

Key words: automatic control, terrain, cluster analysis, mobile robot, speed adaptive control

中图分类号: 

  • TP242

图1

基于改进的GMM地形聚类分析的总体架构"

图2

基于地形聚类分析的机器人速度自适应控制框架"

图3

速度自适应控制器的架构"

图4

实验平台"

图5

典型地形"

图6

聚类准确度与序列长度关系曲线"

图7

两种模型的测试结果"

图8

聚类的混淆矩阵"

图9

机器人以六种（0.2~0.7 m/s）速度在花砖路面运动时的竖直方向加速度曲线"

图10

机器人在花砖地面运动速度与目标函数关系曲线"

图11

机器人在4种典型地形环境下的聚类分析规划运动速度及实际运动速度曲线"

图12

草地速度控制实验结果"

图13

有较大上坡的不平整水泥地速度控制实验结果"

1 Matos-Carvalho J P, Fonseca J M, Mora A, et al. UAV downwash dynamic texture features for terrain classification on autonomous navigation[C]∥Proceedings of the Federated Conference on Computer Science and Information Systems,Poznan,Poland,2018: 1079-1083.
2 Chen Y, Zhang P F, Wang S F, et al. Image feature based machine learning approach for road terrain classification[C]∥Proceedings of IEEE International Conference on Mechatronics and Automation,Changchun, China, 2018: 2097-2102.
3 Vandapel N, Huber D F, Kapuria A, et al. Natural terrain classification using 3-D ladar data[C]∥IEEE International Conference on Robotics and Automation(ICRA), Piscataway, USA, 2004: 5117-5122.
4 Yan W Y, Shaker A, El-Ashmawy N. Urban land cover classification using airborne LiDAR data: a review [J]. Remote Sensing of Environment, 2015, 158: 295-310.
5 Libby J, Stentz A J. Using sound to classify vehicle-terrain interactions in outdoor environments[C]∥ Proceedings of the IEEE International Conference on Robotics and Automation, Piscataway, USA, 2012: 3559-3566.
6 Riopelle N, Caspers P, Sofge D. Terrain classification for autonomous vehicles using Bat-Inspired echolocation[C]∥ Proceedings of the International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018.
7 Iagnemma K, Dubowsky S. Terrain estimation for high-speed rough-terrain autonomous vehicle navigation[C]∥Proc of the International Society for Optical Engineering, Orlando, USA, 2002, 4715: 256-266.
8 Brooks C A, Iagnemma K. Vibration-based terrain classification for planetary exploration rovers[J]. IEEE Transactions on Robotics, 2005, 21(6): 1185-1190.
9 Brooks C A, Iagnemma K. Self-supervised terrain classification for planetary surface exploration rovers [J]. Journal of Field Robotics, 2012, 29(3): 445-468.
10 du Pont E M, Moore C A, Collins J E G, et al. Frequency response method for terrain classification in autonomous ground vehicles[J]. Autonomous Robots2008, 24(4): 337-347.
11 Jitpakdee R, Maneewarn T. Neural networks terrain classification using inertial measurement unit for an autonomous vehicle[C]∥Proceedings of International Conference on Instrumentation, Control and Information Technology, Tokyo, Japan, 2008: 554-558.
12 Weiss C, Fröhlich H, Zell A. Vibration-based terrain classification using support vector machines[C]∥ Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Piscataway, USA, 2006: 4429-4434.
13 Weiss C, Stark M, Zell A. SVMs for vibration-based terrain classification[C]∥Proceedings of the Autonome Mobile Systeme, Kaiserslautern, Germany, 2007: 1-7.
14 魏健,张和明,田雷. 基于车轮力信号的地面类型分类算法研究[J].小型内燃机与车辆技术,2016,45(5): 40-47.
Wei Jian, Zhang He-ming, Tian Lei. Research of ground type classification algorithm based on wheel force signal [J]. Small Internal Combustion Engine and Vehicle Technique, 2016, 45(5):40-47.
15 Weiss C, Tamimi H, Zell A. A combination of vision- and vibration-based terrain classification[C]∥Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Piscataway, USA, 2008: 2204-2209.
16 李强, 薛开, 徐贺,等.基于振动采用支持向量机方法的移动机器人地形分类[J]. 机器人, 2012,34(6): 660-667.
Li Qiang, Xue Kai, Xu He, et al. Vibration-based terrain classification for mobile robots using support vector machine[J]. Robot, 2012, 34(6):660-667.
17 Ojeda L, Borenstein J, Witus G, et al. Terrain characterization and classification with a mobile robot[J]. Journal of Field Robotics, 2006, 23(2): 103-122.
18 周志华. 机器学习[M]. 北京:清华大学出版社,2016.
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