北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (12): 2596-2602.doi: 10.13700/j.bh.1001-5965.2015.0804

• 论文 • 上一篇    下一篇

基于智能驱动器的软体机器人系统

史震云, 朱前成   

  1. 北京航空航天大学 机械工程及自动化学院, 北京 100083
  • 收稿日期:2015-12-04 出版日期:2016-12-20 发布日期:2016-04-18
  • 通讯作者: 史震云,Tel.:010-82356641,E-mail:shichong1983623@hotmail.com E-mail:shichong1983623@hotmail.com
  • 作者简介:史震云,女,博士,讲师。主要研究方向:工业机器人智能装备、智能驱动控制。Tel.:010-82356641,E-mail:shichong1983623@hotmail.com
  • 基金资助:
    中国博士后科学基金(2014M560872)

Soft robot system based on intelligent driver

SHI Zhenyun, ZHU Qiancheng   

  1. School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2015-12-04 Online:2016-12-20 Published:2016-04-18
  • Supported by:
    China Postdoctoral Science Foundation (2014M560872)

摘要: 作为一种新型柔韧机器人,软体机器人越来越受到人们的重视。如何构建在不可预知环境下的应变能力是软体机器人技术的重点研究目标。针对该问题,提出了一种基于智能驱动传感的半软体机器人运动模式和系统组成,在此基础上设计建立了各运动模块的机构构型,并把执行器机构部件和形状记忆合金驱动器耦合成为整体,建立了机器人各关节的动力学模型和运动学模型,根据模型确定了机器人机构设计以及驱动器设计的关键参数。使用高强度工程塑料加工机器人壳体,采用3D打印柔软外壳和非对称足底,将2类合金丝固联在机器人体内,基于径向基函数(RBF)神经网络和支持度函数形成了最终的控制方案,并进行了前进方向的运动试验,验证了该机器人系统模型的正确性。

关键词: 软体机器人, 爬行机器人, 动力学分析, 运动学分析, 形状记忆合金

Abstract: Soft robot, as a new type of flexible robot, is attracting more and more attention. How to build emergency ability in unpredictable environments is the key research goal of soft robot technology. For this problem, an intelligent driving-sensing based motion pattern and system component of semi-soft robot is proposed, and on this basis, the mechanism design configuration of each motion module is designed and established. By coupling the actuators with shape memory alloy drivers into a monolithic structure, dynamic model and kinematics model of robot joints are constituted. According to the model, the key parameters for mechanism design and driver design are determined. Robot shells are manufactured by high strength engineering plastics, and soft enclosure and asymmetric pelma are realized by 3D printing. Two types of alloy wires are fixed in the robot body, the radial basis function (RBF) neural network and support function are used to control the robot, and forward motion is finally tested, which verifies correctness of the proposed robot system model.

Key words: soft robot, crawling robot, dynamic analysis, kinematics analysis, shape memory alloy

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