改进CBBA算法的风电场无人机群巡检任务分配  

TASK ALLOCATION FOR UAV SWARM INSPECTION IN WIND FARMS BASED ON IMPROVED CBBA ALGORITHM

作　　者：焦嵩鸣[1] 陈雨溪 Jiao Songming;Chen Yuxi(Department of Automation,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学自动化系,保定071003

出　　处：《太阳能学报》2024年第10期554-565,共12页Acta Energiae Solaris Sinica

摘　　要：提出一种基于改进的共识捆绑算法(CBBA)的多无人机风电场巡检任务分配方法。首先,根据风电机组基础信息,采用K-均值算法确定无人机数量和安放位置,并构建风电机组巡检价值评估函数,更为合理地评估各风电机组的巡检收益;然后,针对无人机的归巢返程问题,提出一种基于CBBA算法的任务包双向构建策略;最后,为克服传统CBBA算法中存在的因个体“贪婪”导致的整体效益下降问题,设计一种基于类别的奖励制度,以提高任务分配的合理性。试验表明,所提方法可根据风速、风向的变化制定相应的巡检任务分配方案,可为无人机群自主巡检风电场的任务分配提供可参考的方案。Wind turbines are widely distributed and located in complex environments,which are more suitable for autonomous inspection by UAV swarms,where the multi-UAV task allocation problem needs to be solved.In this paper,we propose a multi-UAV wind farm inspection task allocation method based on improved CBBA algorithm.Firstly,based on the basic information of wind turbines,the K-means algorithm is used to determine the number and placement of UAVs,and the value assessment function of wind turbine inspection is constructed to more reasonably evaluate the inspection revenue of each wind turbine;then,a two-way construction strategy of task package based on CBBA algorithm is proposed for the homing and returning problem of UAVs;Finally,to overcome the issue of individual"greed"reducing overall benefits in the traditional CBBA algorithm,a category-based reward system is designed to enhance the rationality of task allocation.The experiments show that the proposed method can develop a corresponding inspection task allocation scheme according to the changes of wind speed and wind direction,and provide a referenceable scheme for the task allocation of autonomous inspection of wind farms by UAV swarms.

关 键 词：风电机组 巡检 无人机 共识捆绑 CBBA算法 

分 类 号：TK83[动力工程及工程热物理—流体机械及工程] V279.2[航空宇航科学与技术—飞行器设计]