基于视觉抓取的并联桁架机器人最优路径控制
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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。
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.
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.
引文
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[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.)
[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,LEIY,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,BAIJ,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.)