[1]周熙炜,尚 宵,尚新娟,等.一种基于云机器人的综合管廊智能巡检调度算法[J].控制与信息技术,2020,(03):18-21.[doi:10.13889/j.issn.2096-5427.2020.03.004]
 ZHOU Xiwei,SHANG Xiao,SHANG Xinjuan,et al.An Intelligent Inspection and Scheduling Algorithm for Integrated Pipe Gallery Based on Cloud Robot[J].High Power Converter Technology,2020,(03):18-21.[doi:10.13889/j.issn.2096-5427.2020.03.004]
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一种基于云机器人的综合管廊智能巡检调度算法()
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《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2020年03期
页码:
18-21
栏目:
机器人
出版日期:
2020-06-05

文章信息/Info

Title:
An Intelligent Inspection and Scheduling Algorithm for Integrated Pipe Gallery Based on Cloud Robot
文章编号:
2096-5427(2020)03-0018-04
作者:
周熙炜尚 宵尚新娟闫茂德王发发
长安大学,陕西西安 710064
Author(s):
ZHOU Xiwei SHANG Xiao SHANG Xinjuan YAN Maode WANG Fafa
Chang ’an University,Xi ’an, Shaanxi 710064, China
关键词:
云机器人管廊巡检自适应权重粒子群-遗传混合算法
Keywords:
cloud robot pipe gallery inspection adaptive weight particle swarm-genetic hybrid algorithm
分类号:
TP242
DOI:
10.13889/j.issn.2096-5427.2020.03.004
文献标志码:
A
摘要:
在先进感知技术的配合下,利用云机器人技术动态卸载复杂数据任务到云端处理,可以提高信息传输效率并提升巡检机器人系统的智能化水平。文章针对管廊运营的瞬发量和缓发量灾害,建立了云机器人巡检区域分配的调度数学模型,并分别利用粒子群和自适应权重粒子群算法对该模型进行求解;为解决算法迭代次数多和实时性差的问题,提出了一种改进的自适应权重粒子群-遗传混合优化算法,其结合粒子群算法的强导向性和遗传算法极强探索精度和求变功能,使得算法迭代次数少,可有效提高管廊内管线泄漏源检测的实时性。仿真结果验证了该算法的正确性,为综合管廊智能巡检提供了一种可行的策略。
Abstract:
With the cooperation of advanced sensing technology, cloud robot technology for dynamically offloading complex data tasks to cloud processing can improve information transfer efficiency and enhance intelligence level of inspection robot system. For the instantaneous and delayed flood disasters of corridor management, a mathematical model of cloud robot inspection area dispatching was established and solved by particle swarm optimization and adaptive weight particle swarm optimization. In order to solve the problem of multiple iterations and poor real-time performance, it proposed an improved adaptive weight particle swarm optimization-genetic hybrid optimization algorithm combined with particle swarm optimization and genetic algorithm. The algorithm has very strong exploring precision and variable function, which makes this algorithm of fewer iterations, and can effectively improve the real-time detection of pipeline leakage source in pipeline corridor. Finally,the algorithm is verified by simulation,which provides a feasible strategy for the integrated inspection of the intelligent corridor.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-11-25
作者简介:周熙炜(1975—),男,博士,副教授,主要从事控制理论与控制工程领域的研究。
基金项目:陕西省重点研发计划(2018GY-065)
更新日期/Last Update: 2020-07-08