摘要
自主导航是衡量机器人智能化水平的核心能力。传统导航框架普遍依赖连续且精确的定位信息,在长廊等感知退化场景中易因定位失效而崩溃。同时,单一规划策略也难以兼顾多变环境下的效率与安全性。为应对此挑战,提出了一种基于点云场景理解与拓扑规划的自适应导航框架。通过基于稀疏体素卷积神经网络场景导航理解的导航策略切换方法,识别开阔区域、狭窄通道和房间等空间结构,设计面向场景特征的导航策略自适应切换方法。提出改进的Zhang-Suen中线提取方法,结合骨架结构剔除冗余节点与分支,增强拓扑地图对环境空间布局的表达能力。设计了一种启发式A^(*)算法,利用中线拓扑实现与通道结构对齐的路径引导,提升机器人在狭窄空间中的通行稳定性与安全裕度。试验结果表明,在狭窄环境中,所提方法较主流局部路径规划方法导航耗时平均减少13.1%,路径平滑度平均提升34.6%,并在定位失效情况下保持稳定、安全运行。
Autonomous navigation is a core capability for evaluating the robot’s level of intelligence.Traditional navigation frameworks heavily rely on continuous and precise positioning information,which often leads to system collapse in perception-degraded environments such as long corridors due to localization failure.Meanwhile,a single planning strategy is insufficient to balance efficiency and safety across diverse environments.To address these challenges,an adaptive navigation framework based on point cloud scene understanding and topological planning is proposed.A navigation strategy switching method based on SPVCNN scene understanding is developed,which effectively recognizes spatial structures such as open areas,narrow corridors,and rooms,designing an adaptive switching approach for scene-feature-oriented navigation strategies.An improved Zhang-Suen skeleton extraction method is introduced,combined with a skeleton-based pruning strategy to remove redundant nodes and branches,thereby enhancing the ability of the topological map to represent environmental spatial layouts.Furthermore,a heuristic A^(*) algorithm is designed,leveraging the extracted skeleton topology to generate path guidance aligned with corridor structures,improving the robot’s stability and safety margin in confined spaces.Experimental results show that,in narrow environments,the proposed method reduces navigation time by an average of 13.1%and improves average path smoothness by 34.6%compared to mainstream local path planning methods,while maintaining stable and safe operation even under localization failure.
作者
郑子瑜
金一凡
吕品
方玮
陈奕璁
袁诚
赖际舟
ZHENG Ziyu;JIN Yifan;LYU Pin;FANG Wei;CHEN Yicong;YUAN Cheng;LAI Jizhou(School of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106;School of Biology and Engineering,Guizhou Medical University,Anshun 561113)
出处
《导航与控制》
2025年第6期39-49,共11页
Navigation and Control
基金
国家自然科学基金(编号:62273178)。
