摘要
针对复杂地形的大型景区安全事故预警问题,设计了一种多风险加权评估法与LKH(Lin-Kernighan-Helsgaun)算法相结合的无人机三维巡检路径规划的策略。首先,栅格化研究区域,综合考虑地形、人流量等事故风险因素,采用熵权法得到景区事故风险程度图。然后,选取高风险区域作为巡检节点,建立以无人机巡检总能耗最小为目标,包含最小步长、最大偏转角等多约束条件的无人机巡检路径规划模型,通过LKH算法进行求解,并优化不满足约束的路径。最后,以南京市钟山风景区为实例,经过风险评估得到25个高风险区域,规划出总长度为8620.66 m、最优能耗为33895.55 J的无人机巡检路径。结果表明,该策略生成路径的能耗更低,且LKH算法在运算时间与稳定性方面均优于蚁群算法、遗传算法和灰狼优化算法,使景区的安全管理更加高效和智能化。
Scenic areas are often characterized by complex terrain,extensive spatial coverage,and various safety risks,which present significant challenges to traditional inspection methods.To enhance early warning capabilities for safety incidents in large scenic areas with intricate landscapes,this study introduces a novel three-dimensional Unmanned Aerial Vehicle(UAV)inspection path planning method grounded in risk assessment.First,the study area is divided into a grid,considering risk factors such as topography and human traffic to create a risk-level map of the scenic area using a weighted assessment method.Next,high-risk areas are identified as inspection nodes,and a UAV inspection path planning model is developed with the objective of minimizing total energy consumption.This model also incorporates UAV performance constraints,including minimum step size and maximum turning angle.The model is solved using the Lin-Kernighan-Helsgaun(LKH)algorithm,with nodes that violate the maximum turning angle constraint being optimized and adjusted accordingly.To validate the proposed method,a case study is conducted at Zhongshan Mountain National Park in Nanjing.The risk assessment identifies 25 high-risk areas,leading to a planned inspection path of 8620.66 m with an optimized energy consumption of 33895.55 J.Additionally,a path correction is implemented for one node that exceeds the maximum turning angle constraint.The experimental results demonstrate that the LKH algorithm significantly enhances computational performance compared to the genetic algorithm,ant colony algorithm,and grey wolf optimization algorithm.Specifically,the LKH algorithm reduces computational time by 96.1%,96.7%,and 50.7%,respectively,while also decreasing energy consumption by 0.4%,3.2%,and 11.8%.Additionally,multiple experimental runs show that the LKH algorithm exhibits a small standard deviation in energy consumption,indicating strong stability and reliability.Overall,this study presents an effective and efficient UAV path planning solution that enhances both safety management and operational capacity for inspections in complex scenic environments.
作者
邬岚
张小奕
陈永鑫
陈茜
陈文栋
WU Lan;ZHANG Xiaoyi;CHEN Yongxin;CHEN Qian;CHEN Wendong(College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China;School of Transportation,Southeast University,Nanjing 211189,China)
出处
《安全与环境学报》
北大核心
2025年第12期4799-4807,共9页
Journal of Safety and Environment
基金
国家重点研发计划项目(2020YFB1600500)。
关键词
安全系统学
景区巡检
风险评估
无人机路径规划
LKH算法
safety systematology
scenic area inspection
risk assessment
UAV path planning
LKH algorithm
