东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (4): 554-558.DOI: -

• 论著 • 上一篇    下一篇

基于ANFIS的挖掘机器人挖掘轨迹仿真

王福斌;刘杰;焦春旺;陈至坤;   

  1. 东北大学机械工程与自动化学院;燕山大学电气工程学院;河北理工大学计算机与自动控制学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50775029);;

Simulating the excavating track of a excavator robot using an adaptive neural-fuzzy inference system

Wang, Fu-Bin (1); Liu, Jie (1); Jiao, Chun-Wang (1); Chen, Zhi-Kun (2)   

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (2) Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China; (3) Computer and Automated Control College, Hebei Polytechnic University, Tangshan 063009, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Wang, F.-B.
  • About author:-
  • Supported by:
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摘要： 为提高液压挖掘机器人工作装置挖掘作业轨迹规划控制精度,将挖掘机器人工作装置简化为斗杆、铲斗两关节二维机械臂进行分析.在建立逆运动学模型时,要将铲斗末端位姿空间与工作装置关节空间和油缸空间联系起来进行轨迹规划,以便在各个空间实现对挖掘机器人的控制.为提高跟踪期望轨迹精度,采用两个自适应神经模糊推理系统(ANFIS)分别学习两个关节的(x,y)坐标与关节角间的逆映射关系,建立了ANFIS逆映射模型.选取逆映射间的输入、输出曲面数据训练ANFIS结构,得到模糊模型的输入、输出映射曲面,实现给定的期望挖掘轨迹,获得相应的关节角.最后将得到的模糊模型用于跟踪期望的运动轨迹,仿真表明跟踪精度能够满足实际要...

关键词: 挖掘机器人, 工作装置模型, 神经模糊推理系统, 轨迹, 仿真

Abstract: To improve tracking pattern control accuracy of the working parts of a hydraulic excavator robot, analysis was conducted on the working parts which were simplified into the two-dimensional robotic arm composed of arm and bucket and two joints. When establishing an inverse kinematics model, terminal position and orientation space of the bucket and joint space and cylinder space of the working parts should relate to the tracking pattern and should control the excavator robot in each space. To improve precision in tracking the desired pattern, two adaptive neural-fuzzy inference systems (ANFIS) were used to determine inverse mapping relations between the x and y joint coordinates and joint angle, and an ANFIS inverse mapping model was established. I/O curve data of inverse mapping was selected to train ANFIS structure, with an I/O mapping curve of a fuzzy model used to obtain a corresponding joint angle based on a given desired excavation trace. Finally, the proposed fuzzy model was used to trace an expected motion pattern, and simulation results showed that tracking precision met actual demands.

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