摘要
为了提升电厂内部巡检效率,本文提出了基于智能机器人的电厂巡检方案。考虑电厂内部场景复杂,实现机器人在未知环境下自主建图的高效性和准确性尤为重要。本文设计一种基于复合探索点的主动SLAM建图方法,基于平面分割和矢量合成法,对探索轨迹施加牵引,减少随机探索带来的地图不确定性并改进边界点评价函数,考虑到边界长度的增益,提高探索效率。首先采用平面分割法在目标点所在区域进行边界点搜索,以运动距离和边界线长度为参数设计评价函数,确定探索范围最大的最佳边界点,并通过最佳边界点和目标点的矢量合成,设计复合探索点,引导机器人同步建图和跟踪。并利用实时定位建图技术,构建当前环境栅格地图,通过探索点的依次探索,最终达到目标点,并完成自主建图,通过再次设定目标点实现跟踪扩大建图范围。所提出的算法具备探索的趋向性,是对栅格地图深度优先搜索,并考虑目标点对其路径存在牵引作用,避免了机器人轨迹多次覆盖和陷入回路的情况。实验结果表明,本方法能以较少的探索次数和较短的探索路径完成未知环境下的目标点跟踪和高精度建图。
To improve the efficiency of internal inspections in power plants,this paper proposes an inspection scheme based on intelligent robots.Given the complexity of power plant environments,achieving efficient and accurate autonomous mapping by robots in unknown settings is crucial.We designed an active SLAM method using composite exploration points,incorporating plane segmentation and vector synthesis to guide exploration trajectories,thereby reducing map uncertainty from random exploration.The boundary point evaluation function is enhanced by considering boundary length gain to improve exploration efficiency.The method involves using plane segmentation to search for boundary points around the target,with an evaluation function based on movement distance and boundary length to determine the optimal boundary point with the largest exploration range.Composite exploration points are created through vector synthesis of the optimal boundary and target points,guiding the robot for simultaneous mapping and tracking.Real-time positioning and mapping technology is used to construct the current environmental grid map,achieving target point tracking and autonomous mapping through sequential exploration points.By setting new target points,tracking and expanding the mapping range are achieved.The proposed algorithm exhibits a tendency towards exploration,performing depth-first search on the grid map while considering the traction effect of target points,thereby avoiding multiple trajectory overlaps and loops.Experimental results demonstrate that this method achieves target tracking and high-precision mapping in unknown environments with fewer exploration steps and shorter paths.
作者
梁天
陈丽
马转转
董哲明
孙中元
Liang Tian;Chen Li;Ma Zhuanzhuan;Dong Zheming;Sun Zhongyuan(College of Air transportation,Shanghai University of Engineering Science,Shanghai 201600,China;Datang International Togtoh Power Generation Co.,Ltd.,Hohhot 010200,China;Suzhou Nuclear Power Research Institute Co.,Ltd.,Suzhou 215004,China)
出处
《电子测量技术》
北大核心
2024年第17期62-70,共9页
Electronic Measurement Technology
基金
临近空间浮空器多场耦合伺服弹性机理研究(52175103)项目资助。
关键词
自主移动机器人
主动SLAM
区域分割
复合探索点
自主建图
autonomous mobile robots
active SLAM
planar segmentation
composite exploration points
autonomous mapping
