摘要
为了尽快发现搜索区域内所有目标,针对群机器人在大范围搜索区域内目标搜索问题,提出了模拟群居生物觅食行为的协同搜索算法。为防止机器人在大范围搜索区域中迷失,将整个搜索区域划分为若干子区域分别搜索;以信号强度和距离为启发信息,借鉴黄蜂群阈值响应模型,建立了区域效用函数并改进,用于确定子区域搜索顺序;对于子区域内群机器人目标搜索问题,模拟群居生物整体快速搜索和局部细致搜索过程,提出了协同搜索算法。经仿真验证,对于区域内不同位置、不同数量的目标,协同搜索算法都能够准确搜索到目标;与随机算法、粒子群算法相比,协同搜索算法能够最快找到目标,且区域内目标数量越多,消耗时间优势越明显。
In order to discover all targets in the searching region,directed at the problem of swarm robots searching targets in large area,coordination searching method which simulates biological foraging behavior is put forward.To prevent robot missing themselves in the large searching area,the whole area is divided to some subdomains.Choosing signal intensity and distance as heuristic information,referring threshold response model of wasp swarm,area utility function is built and improved,which is used to determine searching sequence of subdomains.For targets searching problem in the subdomain,simulating fast search and detailed search process of biological foraging behavior,cooperate search method is raised.Clarified by simulation,for different number of goals in any position,cooperate search method can discover the targets precisely.Compared with random algorithm and particle swarm algorithm,cooperate algorithm can find targets firstly,and the more targets there are,the time cost advantage is more obvious.
作者
李志敏
尹雪峰
LI Zhi-min;YIN Xue-feng(Electronic Information Department,Jiangsu Wuxi Institute of Arts&Technology,Jiangsu Wuxi214200,China;Jiangsu Taodu Secondary Professional School,Jiangsu Yixing214221,China)
出处
《机械设计与制造》
北大核心
2019年第9期222-226,共5页
Machinery Design & Manufacture
基金
江苏省自然科学基金面上项目(BK20151205)
关键词
群机器人系统
协同搜索方法
改进的区域效用函数
Swarm Robots System
Cooperate Search Algorithm
Improved Area Utility Function
