| 摘要: |
| 针对灰狼优化算法易陷入局部最优且单一算法不易解决障碍物空间多机器人隐患搜排的调度问题，提出了一种分步引导式多机器人安全隐患协同排除调度策略。首先引入非线性收敛因子调整策略和静态加权平均权重策略改进灰狼优化算法以避免算法陷入局部最优；随后通过改进的灰狼优化算法先后两次求解遍历顺序，引导机器人规划搜索路径与排除隐患点路径；最后在领航者-跟随者模型的基础上多机器人编队与队形变换避障，逐一到达隐患点位置实现多机器人的调度策略。通过国际通用6个基准函数进行测试，改进的灰狼优化算法在收敛速度、搜索精度及稳定性上均有明显提高，验证了区域多任务安全隐患排除的分步引导式多机器人协同调度策略的有效性。 |
| 关键词: 多机器人 安全隐患排除 调度策略 路径规划 灰狼优化算法算法 |
| DOI： |
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| 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） |
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| Robot scheduling strategy for removing hidden dangers of regional multi-task security |
| LI Jing,YANG Fan |
| (School of Electronics and Information Engineering，Hebei University of Technology，Tianjin 300401，China;a.School of Electronics and Information Engineering;b.Tianjin Key Laboratory of Electronic Materials and Devices，Hebei University of Technology，Tianjin 300401，China) |
| Abstract: |
| In order to solve the problem that gray wolf optimizer algorithm is easy to fall into local optimization and single algorithm is not easy to solve the scheduling problem of obstacle space multi-robot search and remove hidden dangers,a step-by-step guided multi-robot cooperative search and removal of hidden dangers scheduling strategy is proposed.Firstly,nonlinear convergence factor adjustment strategy and static weighted average weight strategy are introduced to improve gray wolf optimizer algorithm to avoid the algorithm falling into local optimization.Then through using the improved gray wolf optimizer algorithm to solve the traversal sequence twice,the robot is guided to plan the search path and eliminate the hidden danger point path.Finally,based on the leader-follower model,the formation and formation transformation of multi-robots are used to avoid obstacles and reach the hidden danger point one by one to realize the scheduling strategy of multi-robot.Experimental simulation shows that the improved gray wolf optimizer algorithm improves the convergence speed,search accuracy and stability significantly by testing six benchmark functions.The effectiveness of the step-by-step guided multi-robot cooperative scheduling strategy is verified. |
| Key words: multiple robots hidden danger removing scheduling strategy path planning gray wolf optimizer algorithm |
《电讯技术》编辑部 