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。
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.
作者
李靖
杨帆
王丽
LI Jing;YANG Fan;WANG Li(School of Electronics and Information Engineering,Hebei University of Technology,Tianjin 300401,China;School of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384,China)
出处
《中国安全生产科学技术》
CAS
CSCD
北大核心
2020年第6期23-29,共7页
Journal of Safety Science and Technology
基金
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)。
关键词
协同搜索
灰狼优化算法
D*算法
路径规划
巡检策略
cooperative search
grey wolf optimizer algorithm
D*algorithm
path planning
inspection strategy
