煤矸石分拣机器人动态目标稳定抓取轨迹规划

马宏伟; 孙那新; 张烨; 王鹏; 曹现刚; 夏晶

doi:10.13272/j.issn.1671-251x.2021110050

煤矸石分拣机器人动态目标稳定抓取轨迹规划

马宏伟^{1, 2,},
孙那新^{1, 2, ,},
张烨^{1, 2},
王鹏^{1, 2},
曹现刚^{1, 2},
夏晶^{1, 2}

1.
西安科技大学机械工程学院, 陕西西安　710054
2.
陕西省矿山机电装备智能监测重点实验室, 陕西西安　710054

基金项目: 国家自然科学基金面上项目（51975468）；国家自然科学基金项目（51705412）。

详细信息

作者简介:
马宏伟(1957-)，男，陕西兴平人，教授，研究方向为智能检测与控制、工业机器人及机电一体化、煤矿机电设备及其自动化、智能化等，E-mail：mahw@xust.edu.cn

通讯作者:
孙那新(1997-)，女，陕西蓝田人，硕士研究生，主要研究方向为机器人视觉伺服控制与轨迹规划，E-mail：1456282619@qq.com

中图分类号: TD713
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出版历程
- 收稿日期: 2021-11-18
- 修回日期: 2022-03-24
- 网络出版日期: 2022-04-05
- 刊出日期: 2022-04-24

Track planning of coal gangue sorting robot for dynamic target stable grasping

MA Hongwei^{1, 2,},
SUN Naxin^{1, 2, ,},
ZHANG Ye^{1, 2},
WANG Peng^{1, 2},
CAO Xiangang^{1, 2},
XIA Jing^{1, 2}

1.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi'an 710054, China

摘要

摘要: 针对机器人分拣煤矸石时，因输送带打滑、左右摆动而造成矸石定位不准确、机械臂末端抓取失败和载荷冲击等问题，提出了一种基于机器视觉的煤矸石分拣机器人动态目标稳定抓取轨迹规划方法。首先，采用基于HU不变矩图像匹配算法对目标矸石进行匹配识别并获取目标矸石位姿；其次，分别建立机器人和相机−机器人运动学方程，并进行正逆求解，实现基于视觉的目标矸石精确定位；最后，采用位置−速度−加速度三环PID控制算法进行目标矸石动态跟踪，即位置环控制器的输入为获取的目标矸石精确位置，位置环控制器的输出作为速度环控制器的输入，速度环控制器的输出作为加速度环控制器的输入，将加速度环控制器的输出叠加到伺服电动机上，使机械臂末端与目标矸石达到位置、速度同步运动的效果，实现平稳快速抓取。采用Matlab对三环PID控制算法、三维比例导引算法和三维偏置比例导引算法进行仿真对比，结果表明：对动态目标的跟踪抓取在追随式、同步式和拦截式3种情况下，三环PID控制算法的响应时间、跟踪抓取时间均较比例导引算法及偏置比例导引算法短，且三环PID控制算法在整个过程中各轴速度、加速度连续、平滑，没有出现突变情况，可实现动态目标同步跟踪、精准抓取。在煤矸石分拣系统平台上应用三环PID控制算法、比例导引算法和偏置比例导引算法进行适应性实验，结果表明：3种算法在机器人运行时各个关节均未超限；三环PID控制算法完成抓取的平均时间比比例导引算法和偏置比例导引算法短；三环PID控制算法在抓取点的平均速度偏差在1 mm/s左右，跟踪速度偏差较小，可满足对高速度目标的同步跟踪、精准抓取要求。
- 煤矸石分拣机器人 /
- 动态目标 /
- 目标矸石跟踪抓取 /
- HU不变矩 /
- 位置−速度−加速度三环PID控制 /
- 拦截式 /
- 追随式 /
- 同步式
Abstract: When the robot is used to sort coal gangue, in order to solve the problems such as inaccurate positioning of gangue, failure of grasping by end of the manipulator and load impact caused by slippage and left-right swing of belt conveyor, a track planning method of coal gangue sorting robot for dynamic target stable grasping based on machine vision is proposed. Firstly, the target gangue is identified and the pose of the target gangue is obtained by using the HU moment invariants image matching algorithm. Secondly, the kinematic equations of the robot and the camera-robot are established respectively, and the forward and inverse solutions are carried out to realize the accurate positioning of the target gangue based on vision. Finally, the position-velocity-acceleration three-loop PID control algorithm is used to dynamically track the target gangue. The input of the position loop controller is the obtained precise position of the target gangue, the output of the position loop controller is used as the input of the velocity loop controller, the output of the velocity loop controller is used as the input of the acceleration loop controller, and the output of the acceleration loop controller is superimposed on the servo motor. Therefore, the end of the manipulator and the target gangue can achieve the effect of synchronous movement of position and velocity, so as to achieve stable and fast grasping. Matlab is used to compare the three-loop PID control algorithm, the three-dimensional proportional navigation algorithm and the three-dimensional biased proportional navigation algorithm. The results show that in the following, synchronous and intercepting cases of the tracking and grasping of dynamic targets, the response time and tracking and grasping time of the three-loop PID control algorithm are better than those of the proportional navigation algorithm and the biased proportional navigation algorithm. And the three-loop PID control algorithm is continuous and smooth in the speed and acceleration of each axis in the whole process without sudden change, which can realize synchronous tracking of dynamic targets and precise grasping. The three-loop PID control algorithm, proportional navigation algorithm and biased proportional navigation algorithm are applied to the coal gangue sorting system platform to carry out adaptability experiments. The results show that the three algorithms do not exceed the limit of each joint during robot operation. The average time of the three-loop PID control algorithm to complete the grasping is shorter than those of the proportional navigation algorithm and the biased proportional navigation algorithm. The average speed error of the three-loop PID control algorithm at the grasping point is about 1 mm/s, and the tracking speed error is small, which can meet the requirements of synchronous tracking and precise grasping of high-speed targets.
- coal gangue sorting robot /
- dynamic target /
- target gangue tracking and grasping /
- HU moment invariants /
- position-velocity-acceleration three-loop PID control /
- intercepting type /
- following type /
- synchronous type

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图 1 煤矸石分拣机器人系统组成

Figure 1. Composition of coal gangue sorting robot system

下载: 全尺寸图片幻灯片

图 2 动态目标稳定抓取轨迹控制流程

Figure 2. Dynamic target stable grasping track control process

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图 3 基于HU不变矩的动态目标匹配流程

Figure 3. Flow chart of dynamic target matching based on HU moment invariants

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图 4 目标矸石匹配结果

Figure 4. Target gangue matching results

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图 5 煤矸石分拣机器人运动学坐标系模型

Figure 5. Kinematics coordinate system model of coal gangue sorting robot

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图 6 坐标系转换关系

Figure 6. Coordinate system transformation diagram

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图 7 追随式3种算法动态目标轨迹规划曲线

Figure 7. Dynamic target track planning curves of three algorithms under following track planning mode

下载: 全尺寸图片幻灯片

图 8 追随式3种算法X轴向位置、速度、加速度变化曲线

Figure 8. X axial position, velocity and acceleration curves of three algorithms under following track planning mode

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图 9 追随式3种算法Y轴向位置、速度、加速度变化曲线

Figure 9. Y axial position, velocity and acceleration curves of three algorithms under following track planning mode

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图 10 追随式3种算法Z轴向位置、速度、加速度变化曲线

Figure 10. Z axial position, velocity and acceleration curves of three algorithms under following track planning mode

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图 11 同步式3种算法动态目标轨迹规划曲线

Figure 11. Dynamic target track planning curves of three algorithms under synchronous track planning mode

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图 12 同步式3种算法X轴向位置、速度、加速度变化曲线

Figure 12. X axial position, velocity and acceleration curves of three algorithms under synchronous track planning mode

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图 13 同步式3种算法Y轴向位置、速度、加速度变化曲线

Figure 13. Y axial position, velocity and acceleration curves of three algorithms under synchronous track planning mode

下载: 全尺寸图片幻灯片

图 14 同步式3种算法Z轴向位置、速度、加速度变化曲线

Figure 14. Z axial position, velocity and acceleration curves of three algorithms under synchronous track planning mode

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图 15 拦截式3种算法动态目标轨迹规划曲线

Figure 15. Dynamic target track planning curves of three algorithms under intercepting track planning mode

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图 16 拦截式3种算法X轴向位置、速度、加速度变化曲线

Figure 16. X axial position, velocity and acceleration curves of three algorithms under intercepting track planning mode

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图 17 拦截式3种算法Y轴向位置、速度、加速度变化曲线

Figure 17. Y axial position, velocity and acceleration curves of three algorithms under intercepting track planning mode

下载: 全尺寸图片幻灯片

图 18 拦截式3种算法Z轴向位置、速度、加速度变化曲线

Figure 18. Z axial position, velocity and acceleration curves of three algorithms under intercepting track planning mode

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图 19 煤矸石分拣机器人实验平台

Figure 19. Experimental platform of coal gangue sorting robot

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表 1 煤矸石分拣机器人运动结构参数

Table 1 Motion structure parameters of coal gangue sorting robot

转换矩阵	运动结构参数
${{\boldsymbol{M}}}_{1}^{{\rm{W}}}$	α₁	l₁	d₁	θ₁
${{\boldsymbol{M}}}_{2}^{1}$	α₂	l₂	d₂	θ₂
${{\boldsymbol{M}}}_{{\rm{E}}}^{2}$	α₃	l₃	d₃	θ₃

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表 2 3种算法运动仿真参数

Table 2 3 kinds of algorithm motion simulation parameters

轨迹规划算法	初始速度 /(m·s⁻¹)	加速度 /(m·s⁻²)	加速时间 /s
偏置比例导引	0	10	0.3
比例导引	0	10	0.3
三环PID控制	0	−	−

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表 3 3种算法实验结果

Table 3 Experimental results of three algorithms

（∆X，∆Y，∆Z）/m	v /(m·s⁻¹)	t_PID /s	t_B /s	t_P /s	E_SPID /mm	E_SB /mm	E_SP /mm	E_VPID /(m·s⁻¹)	E_VB /(m·s⁻¹)	E_VP /(m·s⁻¹)
(0.5，0.5，0.4)	0.9	0.425	0.543	0.556	1.18	0.92	0.82	0.001 5	2.101	2.113
	1.0	0.469	0.564	0.564	1.01	1.03	1.13	0.001 2	2.003	2.011
	1.1	0.486	0.588	0.594	0.92	1.15	0.87	0.000 9	1.904	1.910
(0，0.5，0.4)	0.9	0.301	0.396	0.426	1.11	1.03	0.85	0.000 4	2.106	2.101
	1.0	0.302	0.404	0.450	1.04	1.31	1.16	0.000 6	2.010	2.004
	1.1	0.310	0.414	0.465	0.94	1.03	1.07	0.001 1	1.916	1.905
(−0.5，0.5，0.4)	0.9	0.303	0.372	0.427	1.21	0.90	1.19	0.001 3	2.115	2.102
	1.0	0.308	0.369	0.456	1.06	1.03	1.10	0.000 9	2.013	2.009
	1.1	0.311	0.367	0.492	1.04	0.92	0.97	0.001 4	1.907	1.913

下载: 导出CSV

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