未知环境中无人机自适应边界快速检测算法  

Fast adaptive frontier detection algorithm for in unknown environment

作　　者：唐嘉宁 谢翠娟 赵一帆 李孟霜 彭志祥 TANG Jianing;XIE Cuijuan;ZHAO Yifan;LI Mengshuang;PENG Zhixiang(School of Electrical and Information Engineering,Yunnan Minzu University,Kunming 650000,China;Unmanned Autonomous Systems Institute,Yunnan Minzu University,Kunming 650000,China)

机构地区：[1]云南民族大学电气信息工程学院,昆明650000 [2]云南民族大学无人自主系统研究院,昆明650000

出　　处：《重庆理工大学学报（自然科学）》2023年第9期180-188,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(61963038)。

摘　　要：边界感知检测是无人机实现自主探索的重要组成部分之一。为了提高在复杂多样的地下狭窄环境中自主探索过程的边界检测效率,提出一种未知环境中的无人机自适应边界快速检测算法(ADPlanner)。通过雷达感知地下隧道未知环境,自适应地调整地下隧道或矿洞环境的局部采样空间,根据环境结构大大提高采样率(添加到RRG中的采样点与采样次数的比值);提出重采样率,以减小相邻时刻自适应采样框的采样点冗余度,进而通过重要性采样策略解决GBPlanner重复区域的过采样问题,实现增量检测。仿真实验表明:在2个不同的未知场景中,与GBPlanner相比,ADPlanner边界检测采样的运行时间减少了20.27%~38.33%,路径长度缩短了11.24%~18.86%,总探索时间缩短了27.38%~38.38%,显著提高了无人机在未知环境下的探索效率。Boundary sensing detection is one of the important parts of autonomous exploration of UAV.In order to improve the efficiency of boundary detection in the process of autonomous exploration in complex and diverse underground narrow environments,this paper proposes an adaptive fast boundary detection algorithm for unmanned aerial vehicles(ADPlanner)in unknown environments.First,we perceive the unknown environment of the underground tunnel through optical radar,adaptively adjust the local sampling space of the underground tunnel or mine tunnel environment,and greatly improve the sampling rate(the ratio of the sampling points added to the RRG to the number of sampling attempts)according to the environmental structure.Secondly,we propose a resampling rate to reduce the redundancy of sampling points of the adjacent adaptive sampling frame,Then,the importance sampling strategy is used to solve the oversampling problem of repeated areas in the GBPlanner and achieve incremental detection.The simulation experiment shows that in two different unknown scenarios,compared with the GBPlanner,the ADPlanner boundary detection sampling run time is reduced by 20.27%~38.33%,the path length is reduced by11.24%~18.86%,and the total exploration time is shortened by 27.38%~38.38%,which significantly improves the exploration efficiency of the UAV in the unknown environment.。

关 键 词：未知环境探索 自适应采样框 重要性采样 路径规划 SLAM 

分 类 号：V249[航空宇航科学与技术—飞行器设计]