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[1]李靖,杨帆,王丽.1种机器人工作区域协同搜索避障巡检策略[J].中国安全生产科学技术,2020,16(6):23-29.[doi:10.11731/j.issn.1673-193x.2020.06.004]
　LI Jing,YANG Fan,WANG Li.A cooperative search and obstacle avoidance inspection strategy for robot working area[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(6):23-29.[doi:10.11731/j.issn.1673-193x.2020.06.004]
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1种机器人工作区域协同搜索避障巡检策略
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
16
期数:
2020年6期
页码:
23-29
栏目:
学术论著
出版日期:
2020-06-30

文章信息/Info

Title:
A cooperative search and obstacle avoidance inspection strategy for robot working area
文章编号:
1673-193X(2020)-06-0023-07
作者:
李靖; 杨帆; 王丽
（1.河北工业大学 电子信息工程学院，天津 300401；
2.天津城建大学 计算机与信息工程学院，天津 300384)
Author(s):
LI Jing;  YANG Fan;  WANG Li
(1.School of Electronics and Information Engineering,Hebei University of Technology,Tianjin 300401,China;
2.School of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384,China)
关键词:
协同搜索; 灰狼优化算法; D*算法; 路径规划; 巡检策略
Keywords:
cooperative search;  grey wolf optimizer algorithm;  D* algorithm;  path planning;  inspection strategy
分类号:
X913.4；TP301.6
DOI:
10.11731/j.issn.1673-193x.2020.06.004
文献标志码:
A
摘要:
为解决工作区域搜索待操作目标的不确定性以及单一策略无法解决障碍物区域多搜索目标遍历与避障的问题，提出1种随机与固定搜索相结合的协同巡检策略。该策略在重点监测点通过引入非线性收敛因子及动态权重策略的改进灰狼优化算法遍历多任务点进行固定路线搜索；在非重点监测区域为差速转向移动机器人赋予三视野扫描线，并运用其灵活转动的特点进行随机路线搜索；通过交互式人工标记的方法定位搜索目标点并对其进行标记，运用改进灰狼优化算法对标记出的多目标点进行遍历顺序规划及D*算法避障到达；通过5个国际通用工程函数仿真测试改进的灰狼优化算法。结果表明：改进的灰狼优化算法能加快收敛速度，增强模型的求解精度，加强算法的稳定性，同时验证随机与固定相结合的区域协同搜索避障巡检策略的有效性。
Abstract:
In order to solve the problem about the uncertainty of searching the target to be operated in the working area and the problem that the single strategy can not solve the traversal and obstacle avoidance of multiple search targets in the obstacle area,a cooperative inspection strategy combining the random and fixed search was proposed.In the key monitoring points,the improved gray wolf optimizer algorithm with the nonlinear convergence factor and dynamic weight strategy was introduced to traverse the multiple task points for the fixed route search.In the nonkey monitoring area,the three field scanning line was given to the differential steering mobile robot,and the random path search was carried out by using its characteristic of flexible rotation.Through the method of interactive manual marking,the search target points were located and marked,the improved gray wolf optimizer algorithm was used to carry out the traversal sequence planning for the marked multiple target points,then the D* algorithm was used to avoid the obstacles to reach the multiple target points.The simulation test on the improved gray wolf optimizer algorithm was conducted through five international general engineering functions.The results showed that the improved grey wolf optimizer algorithm could accelerate the convergence speed,enhance the solving accuracy of the model,and improve the stability of the algorithm.At the same time,the effectiveness of the regional cooperative search and obstacle avoidance inspection strategy with the combination of randomness and fixation was verified.

参考文献/References:

［1］颜强,刘琼,王秉.基于循证安全管理的行为安全管理模式研究［J］.中国安全生产科学技术,2019,15(9):110-115. YAN Qiang,LIU Qiong,WANG Bing.Research on behavior-based safety management mode based on evidence based safety management［J］.Journal of Safety Science and Technology,2019,15(9):110-115.
［2］王龙康.煤矿安全隐患层次分析与预警方法研究［D］.北京：中国矿业大学(北京),2015.
［3］罗通元,吴超.伤害事故安全信息认知建模与机理研究［J］.中国安全生产科学技术,2018,14(6):154-159. LUO Tongyuan,WU Chao.Research on modeling and mechanism of safety information cognition for injury accidents［J］.Journal of Safety Science and Technology,2018,14(6):154-159.
［4］李明龙,杨文婧,易晓东,等.面向灾难搜索救援场景的空地协同无人群体任务规划研究［J］.机械工程学报,2019,55(11):1-9. LI Minglong,YANG Wenjing,YI Xiaodong,et al.Swarm robot task planning based on air and ground coordination for disaster search and rescue［J］.Journal of Mechanical Engineering,2019,55(11):1-9.
［5］吴楠,吴庆.面向不确定目标的多无人机协同搜索控制方法［J］.计算机应用与软件,2015,32(2):174-177. WU Nan,WU Qing.Cooperative search control method with multi-UAVS for uncertain targets［J］.Computer Applications and Software,2015,32(2):174-177.
［6］侯岳奇,梁晓龙,何吕龙,等.未知环境下无人机集群协同区域搜索算法［J］.北京航空航天大学学报,2019,45(2):347-356. HOU Yueqi,LIANG Xiaolong,HE Lyulong,et al.Cooperative area search algorithm for UAV swarm in unknown environment［J］.Journal of Beijing University of Areonautics and Astronautics,2019,45(2):347-356.
［7］ORTIGOZA R S, SANCHEZ J R G, GUZMAN V M H,et al.Trajectory tracking control for a differential drive wheeled mobile robot considering the dynamics related to the actuators and power Stage［J］.IEEE Latin America Transactions,2016,14(2):657-664.
［8］ANGEL L,HERNANDEZ C,DIAZ-QUINTERO C.Modeling,simulation and control of a differential steering type mobile robot［C］//Proceedings of the 32nd Chinese Control Conference.IEEE,2013:8757-8762.
［9］MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer［J］.Advances in Engineering Software,2014,69:46-61.
［10］HEIDARI A A,PAHLAVANI P.An efficient modified grey wolf optimizer with Lévy flight for optimization tasks［J］.Applied Soft Computing,2017,60:115-134.
［11］郭振洲,刘然,拱长青,等.基于灰狼算法的改进研究［J］.计算机应用研究,2017(12):3603-3606. GUO Zhenzhou，LIU Ran，GONG Changqing，Study on improvement of gray wolf algorithm［J］.Application Research of Computers,2017(12):3603-3606.
［12］王秋萍,王梦娜,王晓峰.改进收敛因子和比例权重的灰狼优化算法［J］.计算机工程与应用,2019,55(21):60-65,98. WANG Qiuping,WANG Mengna,WANG Xiaofeng.Improved grey wolf optimizer with convergence factor and proportional weight［J］.Computer Engineering and Applications,2019,55(21):60-65,98.
［13］MAUROVIC I,SEDER M,LENAC K,et al.Path planning for active SLAM based on the D* algorithm with negative edge weights［J］.IEEE Transactions on Systems,Man,and Cybernetics:Systems,2018,48（8）:1321-1331.
［14］SHWAIL S H,KARIM A,TURNER S J.Probabilistic multi robot path planning in dynamic environments:a comparison between A* and DFS［J］.International Journal of Computer Applications,2013,82(7):29-34.
［15］AMMAR A,BENNACEUR H,CHAARI I,et al.Relaxed dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments.［J］.Soft Computing,2016,20(10):4149-4171.
［16］PEREZ D,POWLEY E J,WHITEHOUSE D,et al.Solving the physical traveling salesman problem:tree search and macro actions［J］.IEEE Transactions on Computational Intelligence and AI in Games,2017,6(1):31-45.
［17］CAMBAZARD H,CATUSSE N.Fixed-parameter algorithms for rectilinear steiner tree and rectilinear traveling salesman problem in the plane［J］.European Journal of Operational Research,2018,270(2):419-429.
［18］龙文,伍铁斌.协调探索和开发能力的改进灰狼优化算法［J］.控制与决策,2017,32(10):1749-1757. LONG Wen,WU Tiebin.Improved grey wolf optimization algorithm coordinating the ability of exploration and exploitation［J］.Control and Decision,2017,32(10):1749-1757.

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备注/Memo

备注/Memo:
收稿日期： 2019-12-09；网络首发日期： 2020-06-17
* 基金项目： 天津市自然科学基金项目（18JCYBJC16500）；河北省自然科学基金项目（E2016202341）；天津市高等学校基本科研业务费项目（2016CJ12）
作者简介： 李靖，博士研究生，工程师，主要研究方向为智能信息处理。
通信作者： 杨帆，博士，教授，主要研究方向为机器学习。
更新日期/Last Update: 2020-07-07