Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (10): 3095-3100.DOI: 10.11772/j.issn.1001-9081.2020020198

• Frontier & interdisciplinary applications • Previous Articles    

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

REN Na1, ZHANG Nan1, CUI Yan1, ZHANG Rongxue1,2, PANG Xinfu1   

  1. 1. College of Information, Shenyang Institute of Engineering, Shenyang Liaoning 110136, China;
    2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 610100, China
  • Received:2020-02-28 Revised:2020-03-18 Online:2020-10-10 Published:2020-04-02
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61773269), the Overseas Training Project of Liaoning Colleges (2018LNGXGJWPY-YB034).


任娜1, 张楠1, 崔妍1, 张融雪1,2, 庞新富1   

  1. 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
  • 通讯作者: 任娜
  • 作者简介:任娜(1979-),女,辽宁大连人,讲师,硕士,主要研究方向:智能优化方法、无人机航迹规划;张楠(1979-),男,辽宁沈阳人,讲师,硕士,主要研究方向:智能优化方法、社会计算;崔妍(1982-),女,辽宁沈阳人,讲师,博士,主要研究方向:物流优化、智能计算;张融雪(1978-),女,辽宁丹东人,讲师,硕士,主要研究方向:数字媒体;庞新富(1978-),男,辽宁大连人,副教授,博士,主要研究方向:复杂工业系统建模、决策优化。
  • 基金资助:

Abstract: 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.

Key words: Unmanned Aerial Vehicle (UAV), electric power inspection, trajectory control, spatial entity topological network, reinforcement learning

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。

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

CLC Number: