基于机械臂最优轨迹规划理论的电铲安全特性研究

doi:10.16265/j.cnki.issn1003-3033.2025.S1.0027

摘要/Abstract

摘要： 为保证油缸拆装机械臂作业安全,提出基于改进灰狼算法(IGWO)的机械臂最优轨迹求解方法。首先,引入Logistic-Tent映射算法、查分进化算法、种群淘汰进化机制改进标准灰狼(GWO)算法,构建一种用于油缸拆卸机械臂轨迹优化作业的IGWO算法;然后,对比分析IGWO算法与PSO算法和GWO算法在基函数求解问题的准确度,以验证IGWO算法的求优性能;最后,将IGWO算法用于机械臂轨迹优化,并将其与3-5-3多项式轨迹规划作对比。结果表明:采用IGWO算法相较于3-5-3多项式进行轨迹规划作业,最大关节角加速度降低48.3%,提升了机械臂的作业稳定性。

关键词: 油缸, 拆装机械臂, 加速度, 改进灰狼算法(IGWO), 轨迹规划

Abstract:

In order to ensure the operation safety of the cylinder disassembly and assembly manipulator, an optimal trajectory solution method for the manipulator based on the IGWO algorithm was proposed. Firstly, the Logistic-Tent mapping algorithm, the scoring evolution algorithm, and the population elimination evolution mechanism were introduced to improve the standard gray wolf optimization (GWO) algorithm, and an IGWO algorithm for the trajectory optimization of the cylinder disassembly manipulator was obtained. Then, the IGWO algorithm was compared with the PSO algorithm and the GWO algorithm in the basis function solving problem to verify the optimization performance of the IGWO algorithm. Finally, the IGWO algorithm was used for the trajectory optimization of the manipulator and compared with the 3-5-3 polynomial's trajectory planning. The results show that compared with the 3-5-3 polynomial, the IGWO algorithm reduces the maximum angular acceleration of the joint by 48.3%, which improves the operation stability of the manipulator.

Key words: oil cylinder, disassembly and assembly of robotic arms, acceleration, improved gray wolf optimization (IGWO), trajectory planning

中图分类号:

X928事故调查与分析(工伤事故分析与预防)

刘桐, 于涛, 贾建宇. 基于机械臂最优轨迹规划理论的电铲安全特性研究[J]. 中国安全科学学报, 2025, 35(S1): 180-186.

LIU Tong, YU Tao, JIA Jianyu. Research on safety characteristics of electric shovel based on optimal trajectory planning theory of manipulator[J]. China Safety Science Journal, 2025, 35(S1): 180-186.

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参考文献 9

[1]	齐庆杰, 王欢, 董子文, 等. 矿井胶带运输巷火灾蔓延规律的数值模拟研究[J]. 中国安全科学学报, 2016, 26(10):36-41.
	QI Qingjie, WANG Huan, DONG Ziwen, et al. Numerical simulation of belt conveyor fire spreading law in coal mine[J]. China Safety Science Journal, 2016, 26(10):36-41.
[2]	郭维强. 大采高综采覆岩两带发育高度特征研究[J]. 中国安全科学学报, 2022, 32(增2):142-147.
	GUO Weiqiang. Study on characteristics of height of two zones of overburden caused by fully mechanized mining with large mining height[J]. China Safety Science Journal, 2022, 32(S2):142-147. doi: 10.16265/j.cnki.issn1003-3033.2022.S2.0178
[3]	张晓旭, 罗伙根. 寸草塔二矿注氮防灭火数值模拟研究[J]. 中国安全科学学报, 2022, 32(增2):131-135.
	ZHANG Xiaoxu, LUO Huogen. Numerical simulation research on nitrogen injection for fire prevention in Cuncaota No.2 mine[J]. China Safety Science Journal, 2022, 32(S2):131-135. doi: 10.16265/j.cnki.issn1003-3033.2022.S2.0103
[4]	李俊志, 龙腾, 孙景亮, 等. 城市环境下固定翼无人机微分平坦时空分层轨迹规划[J/OL]. 航空学报,1-14[2025-07-10]. http://kns.cnki.net/kcms/detail/11.1929.V.20241218.1006.006.html.
	LI Junzhi, LONG Teng, SUN Jingliang, et al. Differential flatness--based spatial--temporal trajectory optimization for fixed--wing UAVs in urban environments[J]. Acta Aeronautica et Astronautica Sinica,1-14[2025-07-10]. http://kns.cnki.net/kcms/detail/11.1929.V.20241218.1006.006.html.
[5]	张旭辉, 汤杜炜, 杨文娟, 等. 基于改进GWO算法的掘进机断面成形轨迹规划方法研究[J]. 工程设计学报, 2025, 32(3):296-307.
	ZHANG Xuhui, TANG Duwei, YANG Wenjuan, et al. Research on trajectory planning method for roadheader section forming based on improved GWO algorithm[J]. Chinese Journal of Engineering Design, 2025, 32(3):296-307.
[6]	刘建林, 黄海松, 范青松, 等. 基于改进樽海鞘群算法的机械臂多目标轨迹规划研究[J/OL]. 中国机械工程,1-13[2025-07-10]. http://kns.cnki.net/kcms/detail/42.1294.th.20241031.1351.007.html.
	LIU Jianlin, HUANG Haisong, FAN Qingsong, et al. Multi--objective trajectory planning of robotic arms based on improved salp swarm algorithm[J/OL]. China Mechanical Engineering,1-13[2025-07-10]. http://kns.cnki.net/kcms/detail/42.1294.th.20241031.1351.007.html.
[7]	王桂荣, 倪志强, 周坤, 等. 多策略改进粒子群算法的机械臂时间最优轨迹规划[J]. 中国机械工程, 2025, 36(5):1 044-1 053.
	WANG Guirong, NI Zhiqiang, ZHOU Kun, et al, Time--optimal trajectory planning of robotic arms based on MIPSO algorithm[J]. China Mechanieal Engineering, 2025, 36(5):1044-1053.
[8]	张君, 李万业, 郭家华, 等. 钻锚机器人机械臂轨迹规划方法研究[J]. 矿业科学学报, 2024, 9(5):807-816.
	ZHANG Jun, LI Wanye, GUO Jiahua, et al. Research on trajectory planning method of anchor drilling robot manipulator[J]. Journal of Mining Science and Technology, 2024, 9(5):807-816.
[9]	王策, 孙军锋, 徐贞, 等. 基于PSO算法的工业机器人轨迹优化研究[J/OL]. 机械设计,1-9[2025-07-10].https://doi.org/10.13841/j.cnki.jxsj.20240415.001.
	WANG Ce, SUN Junfeng, XU Zhen, et al. Research on trajectory optimization of industrial robots based on PSO algorithm[J/OL]. Mechanical Design,1-9[2025-07-10].https://doi.org/10.13841/j.cnki.jxsj.20240415.001.

函数	表达式	维数	定义域
F₁	${F}_{1}\left(x\right)=ma{x}_{i}\left\{\right\|{x}_{i}\|,1\le i\le 30\}$	30	[-100,100]
F₂	${F}_{2}\left(x\right)=\sum _{i=1}^{30}\|{x}_{i}\|+\prod _{i=1}^{30}\|{x}_{i}\|$	30	[-10,10]
F₃	${F}_{3}\left(x\right)={\left[\frac{1}{500}+\sum _{j=1}^{25}\frac{1}{j+\sum _{i=1}^{2}({x}_{i}-{a}_{ij}{)}^{6}}\right]}^{-1}$	2	[-65,65]
F₄	${F}_{4}\left(x\right)=\sum _{i=1}^{30}[{x}_{i}^{2}-10cos\left(2\pi {x}_{i}\right)+10]$	30	[-5,5]

函数	方法	平均值	最差值	最优值	标准值
F₁	PSO	1.253 2	1.511 2	0.956 2	0.253 2
	GWO	7.26×10^-8	6.52×10^-6	2.61×10^-8	2.51×10^-6
	IGWO	2.51×10^-10	1.45×10^-9	2.15×10^-11	5.33×10^-10
F₂	PSO	6.53×10^-2	5.33×10^-1	2.56×10^-3	0.05151
	GWO	2.63×10^-17	6.33×10^-15	2.93×10^-17	2.33×10^-17
	IGWO	6.33×10^-25	6.83×10^-25	2.33×10^-26	2.33×10^-25
F₃	PSO	1.662 5	5.925 6	0.998 2	1.766 5
	GWO	3.252 2	10.225 2	0.998 2	3.525 6
	IGWO	1.256 2	2.951 5	0.998 2	0.625 2
F₄	PSO	56.012 5	92.256 3	31.526 3	15.256 2
	GWO	4.263 2	22.133 6	2.02×10^-15	6.2531
	IGWO	2.33×10^-14	4.42×10^-13	0	1.256 6

关节位置	q/(°)
关节位置	1	2	3	4
位置0	-10.22	-10.12	-14.65	69.32
位置1	-30.22	-6.32	-12.62	70.65
位置2	-40.33	-3.25	-11.66	80.97
位置3	-48.33	-0.22	-10.66	95.27
位置4	-60.66	1.64	-6.28	100.64
位置5	-70.52	5.61	2.33	110.66
位置6	-90.23	7.87	7.26	120.96