• CN：11-2187/TH
  • ISSN：0577-6686
机械工程学报

机械工程学报 ›› 2015, Vol. 51 ›› Issue (3): 18-28.doi: 10.3901/JME.2015.03.018

• 机构学及机器人 • 上一篇    下一篇

扫码分享

基于多目标优化的矿用救援机器人动力匹配

刘建, 葛世荣, 朱华, 唐超权   

  1. 中国矿业大学机电学院
  • 出版日期:2015-02-05 发布日期:2015-02-05
  • 基金资助:
    国家高技术研究发展计划资助项目(863计划，2012AA041504)

Mine Rescue Robot Power Matching Based on Multi-objective Particle Swarm Optimization

LIU Jian, GE Shirong, ZHU Hua, TANG Chaoquan   

  1. School of Mechatronic Engineering, China University of Mining and Technology
  • Online:2015-02-05 Published:2015-02-05

PDF

413

摘要： 目前，矿用救援机器人只有通过动力系统参数的合理匹配解决其在复杂恶劣的井下环境中无法随时补充能源及防爆电池组对机器人动力性能影响严重的问题。为此，提出基于多目标粒子群优化算法的动力匹配设计方法。该方法根据矿用救 援机器人动力性能要求，确定了其动力系统参数匹配的优化目标和约束条件；基于履带行驶动力学，并考虑防爆电池组对 机器人动力性能的影响，建立矿用救援机器人动力匹配模型，确定动力系统参数匹配多目标优化的决策变量；通过多目标粒子群优化算法确定了矿用救援机器人动力系统参数匹配的合理取值范围。通过试验与优化前的设计相比，机器人的总质量降低了24.36%，续航时间增加了1倍，进而验证了该方法能够有效、快速地解决矿用救援机器人动力系统参数的合理匹配 问题。

关键词: 动力匹配, 多目标优化, 粒子群算法, 矿用救援机器人

Abstract: At present, the coal mine rescue robot is without the power supply in the hazard and complex coal mine and the explosive-proof batteries unit influences dynamic performance very seriously. It is the only method to solve the two problems above by power matching and optimization. Therefore, the power matching design method based on multi-objective particle swarm optimization (PSO) is proposed. The optimization goal and constraint condition for power matching are confirmed by the analysis of the robot dynamic performance requirement. And the model of the robot power matching is established and the decision variables of multi-objective optimization are ensured, which are based on the tracked vehicle dynamic and the influence for the dynamic performance by explosive-proof batteries unit. Finally, it is calculated which the reasonable value range of the parameters of power matching. The test indicates that the mass of the coal mine rescue robot has reduced by 24.36% and the time of endurance is double. As well as, it is supported the multi-objective particle swarm optimization could effectively and rapidly solve the power matching of the coal mine rescue robot.

Key words: multi-objective optimization, particle swarm optimization, power matching, mine rescue robot

中图分类号: 

版权所有 © 2019 《机械工程学报》编辑部 | 京ICP备05055788号-30