基于视觉抓取的并联桁架机器人最优路径控制

doi:10.11772/j.issn.1001-9081.2018071586

计算机应用 ›› 2019, Vol. 39 ›› Issue (3): 913-917.DOI: 10.11772/j.issn.1001-9081.2018071586

• 应用前沿、交叉与综合 • 上一篇下一篇

基于视觉抓取的并联桁架机器人最优路径控制

杨继东, 孙兆琦, 王飞龙

重庆大学机械工程学院, 重庆 400044

收稿日期:2018-08-02 修回日期:2018-09-17 出版日期:2019-03-10 发布日期:2019-03-11
通讯作者: 孙兆琦
作者简介:杨继东(1966-),男,重庆人,副教授,硕士,主要研究方向:机电一体化设计、智能制造及装备;孙兆琦(1992-),男,河北张家口人,硕士研究生,主要研究方向:自动化控制、机器视觉、智能算法;王飞龙(1994-),男,安徽阜阳人,硕士研究生,主要研究方向:自动化控制、机电一体化设计。
基金资助:
国家自然科学基金资助项目（51375507）。

Optimal path control of parallel truss manipulator based on visual grasping

YANG Jidong, SUN Zhaoqi, WANG Feilong

College of Mechanical Engineering, Chongqing University, Chongqing 400044, China

Received:2018-08-02 Revised:2018-09-17 Online:2019-03-10 Published:2019-03-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (51375507).

摘要/Abstract

摘要：

针对元件的抓取路径规划问题，提出一种以最小化时间为目的，结合蚁群算法和禁忌搜索算法的混合优化算法。首先，将基于机器视觉抓取元件的问题确定为有约束的旅行商问题（TSP）；然后，分析了元件大小和抓取放置过程对于路径规划的综合影响，对路径选择概率和禁忌域进行了适应性改进；其次，一方面引入了2-opt局部优化以及信息素惩罚、奖励机制以改善蚂蚁的搜索能力，另一方面对信息挥发因子作适应性改进以提高蚂蚁的自适应能力；最后，针对基本算法和改进的混合优化算法，仿真实验和平台实验分别进行了性能指标和抓取时间的对比分析。实验结果表明，仿真环境下，与蚁群优化（ACO）算法和禁忌搜索（TS）算法相比，混合优化算法的平均迭代次数降低了约50%，且其他性能较为优越，平台测试的抓取用时测试结果也说明了混合优化算法较随机结果和基本算法的优越性，可以快速完成元件抓取任务。

关键词: 机器视觉, 有约束的TSP, 最优路径, 蚁群算法, 混合优化算法

Abstract:

To solve the problem of component grasping path planning, a hybrid optimization algorithm based on ant colony algorithm and tabu search algorithm was proposed to minimize the time. Firstly, the problem of grasping components based on machine vision was defined as Traveling Salesman Problem (TSP) with precedence constraint. Secondly, comprehensive effects of the sizes of components and the grasping and placement process on path planning were analyzed, and the path selection probability and tabu region were improved adaptively. Thirdly, on the one hand, 2-opt local optimization, pheromone punishment and reward mechanism were introduced to improve the search ability of ants; on the other hand, pheromone evaporation factor was improved adaptively to increase the adaptability of ants. Finally, for the basic algorithm and the improved hybrid optimization algorithm, the performance index and grasping time were compared and analyzed by simulation experiment and platform experiment. The experimental results show that, compared with Ant Colony Optimization (ACO) algorithm and Tabu Search (TS) algorithm, the average iteration times of the proposed hybrid optimization algorithm is reduced by about 50%, and other performances are superior to the other algorithms. The results of platform test also show that the hybrid optimization algorithm is superior to random results and basic algorithm, and it can realize component grasping task quickly.

Key words: machine vision, Traveling Salesman Problem (TSP) with precedence constraint, optimal path, ant colony algorithm, hybrid optimization algorithm

中图分类号:

TP273
TP18

杨继东, 孙兆琦, 王飞龙. 基于视觉抓取的并联桁架机器人最优路径控制[J]. 计算机应用, 2019, 39(3): 913-917.

YANG Jidong, SUN Zhaoqi, WANG Feilong. Optimal path control of parallel truss manipulator based on visual grasping[J]. Journal of Computer Applications, 2019, 39(3): 913-917.

参考文献

[1] 李尧,石红瑞.基于图像识别和遗传算法的Tripod机器人最优轨迹控制[J].计算机应用,2016, 36(S1):106-109.(LI Y, SHI H Z. Optimal trajectory control of the Tripod robot based on image recognition and genetic algorithm[J]. Journal of Computer Applications, 2016, 36(S1):106-109.)
[2] DORIGO M, GAMBARDELLA M L. Ant colonies for the travelling salesman problem[J]. Biosystems, 1997,43(2):73-81.
[3] 叶小勇,雷勇,侯海军.蚁群算法在全局最优路径寻优中的应用[J].系统仿真学报, 2007,19(24):5643-5647. (YE X Y, LEI Y, HOU H J. Application of ant colony algorithm in global optimal path searching[J]. Journal of System Simulation, 2007, 19(24):5643-5647.)
[4] 张慕雪,张达敏,杨菊蜻,等.基于禁忌搜索的蚁群算法优化算法[J].通信技术,20178, 50(8):1658-1663. (ZHANG M X, ZHANG D M, YANG J Q, et al. Ant colony optimization algorithm based on tabu search[J]. Communications Technology, 2017, 50(8):1658-1663.)
[5] 陈丹,白军,石国良.一种新的四自由度SCARA机器人手眼标定方法[J].传感器与微系统,2018,37(2):72-75.(CHEN D, BAI J,SHI G L. A new method of four DOF SCARA robot hand-eye calibration[J]. Transducer and Microsystem Technologies, 2018, 37(2):72-75.)
[6] 高海昌,冯博琴,朱利.智能优化算法求解TSP问题[J].控制与决策,2006,21(3):241-247.(GAO H C, FENG B Q, ZHU L. Reviews of the meta-heuristic algorithms for TSP[J]. Control and Decision, 2006, 21(3):241-247.)
[7] 吴斌,史忠植.一种基于蚁群算法的TSP问题分段求解算法[J].计算机学报,2001,24(12):1328-1333.(WU B, SHI Z Z. An ant colony algorithm based partition algorithm for TSP[J]. Chinese Journal of Computers, 2001, 24(12):1328-1333.)
[8] MULLEN R J, MONEKOSSO D, BARMAN S, et al. A review of ant algorithms[J]. Expert Systems with Applications, 2009, 36(6):9608-9617.
[9] 吴庆洪,张颖,马宗民.蚁群算法综述[J].微计算机信息,2011,27(9):1-2. (WU Q H, ZHANG Y, MA Z M. Review of ant colony optimization[J]. Microcomputer Information, 2011, 27(9):1-2.)
[10] CIORNEI I, KYRIAKIDES E. Hybrid ant clony-genetic algorithm (GAAPI) for global continuous optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 2012, 42(1):234-245.
[11] 张鹏,魏云霞,薛宏全,等.基于优胜劣汰规则的异类多种群蚁群算法[J].计算机工程,2012,38(18):182-185.(ZHANG P, WEI Y X, XUE H Q, et al. Heterogeneous multiple colonies ant colony algorithm based on survival of fittest rules[J]. Computer Engineering, 2012, 38(18):182-185.)
[12] 王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001:67-68.(WANG L. Intelligent Optimization Agorithm and Its Application[M]. Beijing:Tsinghua University Press, 2001:67-68.)
[13] 刘兴,贺国光.车辆路径问题的禁忌搜索算法研究[J].计算机工程与应用,2007,43(24):179-181. (LIU X, HE G G. Study on tabu search algorithm for stochastic vehicle routing problem[J]. Computer Engineering and Applications, 2007, 43(24):179-181.)
[14] 彭茂.一种求解TSP问题的改进禁忌搜索算法[J].计算技术与自动化,2012,31(1):78-81.(PENG M. Improved tabu search algorithm for solving traveling salesman problem[J]. Computing Technology and Automation, 2012, 31(1):78-81.)
[15] 张勇.基于改进蚁群算法物流配送路径优化的研究[J].控制工程,2015,22(2):252-25.(ZHANG Y. Study of optimizing logistic distribution routing based on improved ant colony algorithm[J]. Control Engineering of China, 2015, 22(2):252-256.)

基于视觉抓取的并联桁架机器人最优路径控制

Optimal path control of parallel truss manipulator based on visual grasping

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张亚洲, 卢先领. 基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法[J]. 计算机应用, 2020, 40(5): 1545-1552.
[2]	段宇君, 王耀力, 常青, 刘鑫. 改进型蚁群算法融合混沌优化的pSPIEL算法的无线传感器布局优化[J]. 计算机应用, 2020, 40(3): 793-798.
[3]	屈立成, 吕娇, 赵明, 王海飞, 屈艺华. 基于三维时空地图和运动分解的多机器人路径规划算法[J]. 计算机应用, 2020, 40(12): 3499-3507.
[4]	刘子威, 邓春华, 刘静. 基于非对称沙漏网络结构的目标检测算法[J]. 计算机应用, 2020, 40(12): 3526-3533.
[5]	郜明, 任德均, 胡云起, 付磊, 邱吕. 基于机器视觉和轻量级卷积网络的安瓿瓶包装质量检测算法[J]. 计算机应用, 2020, 40(10): 2899-2903.
[6]	刘育良, 陈淮莉. 负载影响下纯电动汽车配送路径规划及充电策略[J]. 计算机应用, 2020, 40(10): 2831-2837.
[7]	王东先, 孟学雷, 乔俊, 汤霖, 焦志臻. 基于改进蚁群算法的铁路乘务交路计划的编制[J]. 计算机应用, 2019, 39(9): 2749-2756.
[8]	李卓, 李引珍, 李文霞. 应急物资运输路径多目标优化模型及求解算法[J]. 计算机应用, 2019, 39(9): 2765-2771.
[9]	石昌友, 王美丽, 刘欣然, 黄慧丽, 周德强, 邓干然. 基于机器视觉的不同类型甘蔗茎节识别[J]. 计算机应用, 2019, 39(4): 1208-1213.
[10]	郑延斌, 王林林, 席鹏雪, 樊文鑫, 韩梦云. 基于蚁群算法及博弈论的多Agent路径规划算法[J]. 计算机应用, 2019, 39(3): 681-687.
[11]	郭良敏, 朱莹, 孙丽萍. 障碍空间中基于并行蚁群算法的k近邻查询[J]. 计算机应用, 2019, 39(3): 790-795.
[12]	王东先, 孟学雷, 何国强, 孙慧萍, 王喜栋. 基于改进蚁群算法的铁路乘务排班计划编制[J]. 计算机应用, 2019, 39(12): 3678-3684.
[13]	刘保成, 朴燕, 唐悦. 基于时空正则化的视频序列中行人的再识别[J]. 计算机应用, 2019, 39(11): 3216-3220.
[14]	张雲轲, 刘丹. 基于小型Zynq SoC硬件加速的改进TINY YOLO实时车辆检测算法实现[J]. 计算机应用, 2019, 39(1): 192-198.
[15]	马梦莹, 曾雅丽, 魏甜甜, 陈志德. 基于邻居节点预状态的无线传感器网络故障诊断算法[J]. 计算机应用, 2018, 38(8): 2348-2352.