基于改进蜂群算法的工业机器人路径规划研究
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摘要
针对工业机器人在复杂环境中运动的避障及路径优化问题,提出基于改进人工蜂群算法的工业机器人避障路径规划策略。首先针对传统人工蜂群算法搜索能力不足且容易陷入局部最优的问题,将禁忌搜索思想引入到人工蜂群算法最优解搜索过程中,形成了基于禁忌搜索的改进型人工蜂群算法,然后将其应用到工业机器人的路径规划问题中,并进行了仿真实验。结果表明,改进后的方法能够得到最优的路径,且寻优速度快、过程稳定。该方法可用于解决工业机器人路径规划问题。
Aiming at the obstacle avoidance and path optimization for industrial robots in complex environments, a path planning strategy for robot obstacle avoidance based on improved artificial bee colony algorithm is proposed. Firstly, aiming at the problems of lack of search ability and easy to fall into local optimum for traditional artificial bee colony algorithm, tabu search is introduced into the search process of artificial bee colony algorithm. An improved artificial bee colony algorithm based on tabu search is formed, and applied to the path planning problem of industrial robot. The results show that the improved method can obtain the optimal obstacle path of industrial robot, and the optimization speed is fast and the process is stable, which can be used to solve the problem of obstacle avoidance path planning of g industrial robot.
Aiming at the obstacle avoidance and path optimization for industrial robots in complex environments, a path planning strategy for robot obstacle avoidance based on improved artificial bee colony algorithm is proposed. Firstly, aiming at the problems of lack of search ability and easy to fall into local optimum for traditional artificial bee colony algorithm, tabu search is introduced into the search process of artificial bee colony algorithm. An improved artificial bee colony algorithm based on tabu search is formed, and applied to the path planning problem of industrial robot. The results show that the improved method can obtain the optimal obstacle path of industrial robot, and the optimization speed is fast and the process is stable, which can be used to solve the problem of obstacle avoidance path planning of g industrial robot.
引文
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[14] 刘蓓蕾,江铭炎,张振月.基于禁忌搜索的人工蜂群算法及应用[J].计算机应用研究,2015,32(7):2005-2008.
[15] 李阳,范厚明,张晓楠,等.求解模糊需求车辆路径问题的两阶段变邻域禁忌搜索算法[J].系统工程理论与实践,2018,38(2):522-531.
[2] 游晓明,刘升,吕金秋.一种动态搜索策略的蚁群算法及其在机器人路径规划中的应用[J].控制与决策,2017,32(3):552-556.
[3] 屈鸿,黄利伟,柯星.动态环境下基于改进蚁群算法的机器人路径规划研究[J].电子科技大学学报,2015,44(2):260-265.
[4] 温素芳,郭光耀.基于改进人工势场法的移动机器人路径规划[J].计算机工程与设计,2015,36(10):2818-2822.
[5] 曾明如,徐小勇,刘亮,等.改进的势场蚁群算法的移动机器人路径规划[J].计算机工程与应用,2015,51(22):33-37.
[6] 姚江云,孔峰,王娟.工业机器人最短移动路径智能选取方法仿真[J].计算机仿真,2018,35(3):248-251,360.
[7] PAN Q K,WANG L,LI J Q,et al.A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation[J].Omega,2014,45(C):42-56.
[8] GAO W F,LIU S Y,HUANG L L.Enhancing artificial bee colony algorithm using more information-based search equations[J].Information Sciences,2014,270:112-133.
[9] OZTURK C,HANCER E,KARABOGA D.A novel binary artificial bee colony algorithm based on genetic operators[J].Information Sciences,2015,297:154-170.
[10] 王海泉,胡瀛月,廖伍代,等.基于改进人工蜂群算法的机器人路径规划[J].控制工程,2016,23(9):1407-1411.
[11] WU B,QIAN C H.Differential artificial bee colony algorithm for global numerical optimization[J].Journal of computer,2011,6(5):841-848.
[12] 于霜,丁力,吴洪涛.基于改进人工蜂群算法的无人机的航迹规划[J].电光与控制,2017,24(1):19-23.
[13] 陈诗军,王慧强,陈大伟,等.基于改进禁忌搜索的基站布局优化算法[J].计算机工程与科学,2018,40(2):341-347.
[14] 刘蓓蕾,江铭炎,张振月.基于禁忌搜索的人工蜂群算法及应用[J].计算机应用研究,2015,32(7):2005-2008.
[15] 李阳,范厚明,张晓楠,等.求解模糊需求车辆路径问题的两阶段变邻域禁忌搜索算法[J].系统工程理论与实践,2018,38(2):522-531.