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代价函数引导的机械臂运动规划算法

徐晓慧 张金龙

徐晓慧, 张金龙. 代价函数引导的机械臂运动规划算法[J]. 机械科学与技术, 2020, 39(1): 62-67. doi: 10.13433/j.cnki.1003-8728.20190099
引用本文: 徐晓慧, 张金龙. 代价函数引导的机械臂运动规划算法[J]. 机械科学与技术, 2020, 39(1): 62-67. doi: 10.13433/j.cnki.1003-8728.20190099
Xu Xiaohui, Zhang Jinlong. A Robot Arm Motion Planning Algorithm Guided by Cost Function[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(1): 62-67. doi: 10.13433/j.cnki.1003-8728.20190099
Citation: Xu Xiaohui, Zhang Jinlong. A Robot Arm Motion Planning Algorithm Guided by Cost Function[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(1): 62-67. doi: 10.13433/j.cnki.1003-8728.20190099

代价函数引导的机械臂运动规划算法

doi: 10.13433/j.cnki.1003-8728.20190099
基金项目: 

江苏省自然科学基金项目 BK2009406

江苏开放大学"十三五"规划课题项目 16SSW-Q-001

详细信息
    作者简介:

    徐晓慧(1991-), 讲师, 硕士研究生, 研究方向为机器人技术, xuxiaoyang910829@163.com

  • 中图分类号: TP242

A Robot Arm Motion Planning Algorithm Guided by Cost Function

  • 摘要: 本文提出一种基于连杆运动方程的人工势场,引导基于转换的快速随机扩展树(T-RRT)改进算法采样,在高维度空间搜索低成本路径的同时解决机械臂运动规划中T-RRT算法收敛速度慢的问题。简化机械臂模型以提高碰撞检测的效率,并与运动学分析结合调制连杆运动方程,从而确定各质点轨迹长度、叠加以建立机械臂人工势场,作为代价函数判断状态节点的成本,引导其不断向目标位置逼近,同时为了进一步提高算法的扩张速度,引入剪枝函数对细化节点进行限制。在不同的障碍地图中进行MATLAB仿真实验,该算法与RRT、T-RRT算法相比,路径长度最短、节点采样效率最高、节点平均采样时间最优,运行时间分别缩短了约3/4及2/3。所提算法在提高路径质量的同时有效提高搜索效率,能适应环境的变化。
  • 图  1  机械臂简化模型

    图  2  机械臂运动学模型

    图  3  T-RRT改进算法

    图  4  转换测试流程图

    图  5  多种地图环境下机械臂运动规划

    表  1  连杆参数

    i 1 2 3 4 5 6
    ai/cm 0 9 9 9 0 0
    di/cm 0 0 0 0 0 7
    αi/(°) 90 0 0 0 90 0
    θi/(°) 0 0 0 0 0 0
    下载: 导出CSV

    表  2  各种算法实验结果对比

    算法 简单障碍环境(图 5a)) 复杂障碍环境(图 5b))
    t/s L n nall S/% t/s L n nall S/%
    1 2.494 1 3 013.4 67 10 707 100 3.819 4 3 952.0 236 13 253 99
    2 1.534 5 1 711.0 64 3 234 100 2.780 2 2 402.8 169 4 642 100
    3 0.574 6 1 666.1 62 2 822 100 0.928 2 207.1 169 3 557 100
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-16
  • 刊出日期:  2020-01-01

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