面向无人机电力巡检的语义实体构建及航迹控制方法  

Method of semantic entity construction and trajectory control for UAV electric power inspection

作　　者：任娜 张楠 崔妍 张融雪 庞新富 REN Na;ZHANG Nan;CUI Yan;ZHANG Rongxue;PANG Xinfu(College of Information,Shenyang Institute of Engineering,Shenyang Liaoning 110136,China;College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing Jiangsu 610100,China)

机构地区：[1]沈阳工程学院信息学院,沈阳110136 [2]南京航空航天大学计算机科学与技术学院,南京610100

出　　处：《计算机应用》2020年第10期3095-3100,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61773269);辽宁省高等学校国(境)外培养项目(2018LNGXGJWPY-YB034)。

摘　　要：航迹的合理控制是影响无人机(UAV)智能决策重要因素。考虑UAV巡检的局部观测性和任务环境的高空复杂性,以电力巡检领域知识为背景,提出面向UAV电力巡检的语义实体构建及航迹控制方法。首先,基于电力巡检领域的实体知识构建空间拓扑网络,并生成关于位置节点的语义航迹序列网络及其语义接口;然后,根据空间拓扑结构相似性度量的结果集,提出安全许可机制和基于强化学习的航迹控制策略,实现UAV电力巡检在统一的概念内涵和位置结构上的轨迹控制。实验结果表明:作为UAV巡检的实例,所提方法得到的最优策略能获得最大化的鲁棒性能;同时,该方法通过强化学习方法使目标网络的适应度稳定收敛且实体区域覆盖率高于95%,为UAV电力巡检任务决策提供了飞行依据。The reasonable control of trajectory is an important factor affecting intelligent decision-making of Unmanned Aerial Vehicle(UAV).Focusing on the local observability and the complexity of upper air of mission environment,a method of semantic entity construction and trajectory control for UAV electric power inspection was proposed.Firstly,a spatial topology network based on entity knowledge of electric power inspection field was built,and the semantic trajectory sequence network about position nodes and its semantic interfaces were generated.Then,based on the result set of similarity measure of spatial topology structures,the security licensing mechanism and reinforcement learning based trajectory control strategy were proposed to realize the UVA electric power inspection on the basis of consensus concept connotation and position structure.Experimental results show that for an example of UAV electric power inspection,the optimal strategy obtained by the proposed method can satisfy the maximum robust performance,and at the same time,the fitness of the target network can stably converge and the physical area coverage is higher than 95%through the reinforcement learning of this method,so that the method provides flight basis for the decision-making of UVA electric power inspection tasks.

关 键 词：无人机 电力巡检 航迹控制 空间实体拓扑网 强化学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]