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激光雷达巷道中线提取与差速小车双环控制实现
集成电路应用
许志远,侯浩天,岳斌斌,张亚坤
黄河交通学院智能制造与电气工程学院
摘要: 针对巷道等狭窄环境下自动导引车高精度居中行驶的需求,设计了一种嵌入式差速小车系统。系统以STM32H7为主控,搭载M10P PHY二维激光雷达及带编码器的减速电机。雷达通过网口UDP协议实时传输点云数据至STM32H7,经解析后生成巷道的占据栅格图。采用ZhangSuen细化算法提取可通行区域的骨架,获得单像素宽度的中线,并据此计算横向偏差与角度偏差。控制部分采用串级双环PID结构,外环负责位置控制,内环负责角度控制,输出差速转向量以驱动带编码器的减速电机,实现平稳转向。系统同时配备RGB液晶屏,实时显示巷道二维栅格地图与中线。实验结果表明,小车从不同初始偏移位置出发均能快速收敛至巷道中线,控制周期约为30 ms,差速转向过程平滑,显示效果直观,具有良好的工程实用性。
中图分类号:P242.6;TP273文献标识码:ADOI:10.19339/j.issn.1674-2583.2026.07.006
中文引用格式:许志远,侯浩天,岳斌斌,等. 激光雷达巷道中线提取与差速小车双环控制实现[J].集成电路应用,2026,43(7):29-33.
英文引用格式:Xu Zhiyuan,Hou Haotian,Yue Binbin,et al. LiDAR-based roadway centerline extraction and dual-loop control of a differential-drive vehicle[J].Application of IC,2026,43(7):29-33.
LiDAR-based roadway centerline extraction and dual-loop control of a differential-drive vehicle
Xu Zhiyuan,Hou Haotian,Yue Binbin,Zhang Yakun
School of Intelligent Manufacturing and Electrical Engineering, Yellow River Transportation College
Abstract: To meet the demand for high-precision centering driving of automatic guided vehicles in narrow environments such as roadways, an embedded differential-drive vehicle system is designed. The system is based on STM32H7 as the main controller, equipped with M10P PHY 2D LiDAR and a geared motor with an encoder. The LiDAR transmits point cloud data to the STM32H7 in real time via the network port UDP protocol. After parsing, an occupancy grid map of the roadways is generated. The Zhang-Suen thinning algorithm is used to extract the skeleton of the passable area, obtaining a single-pixel-width centerline, and based on this, the lateral deviation and angular deviation are calculated. The control part adopts a cascade double-loop PID structure, with the outer loop responsible for position control and the inner loop for angle control. The differential steering quantity is output to drive the geared motor with an encoder, achieving smooth steering. The system is also equipped with an RGB LCD screen to display the 2-D grid map of the tunnel and the centerline in real time. Experimental results showed that the vehicle could quickly converge to the centerline of the roadways from different initial offset positions, with a control period of approximately 30 ms. The differential steering process was smooth, and the display effect was intuitive, demonstrating good engineering practicability.
Key words : LiDAR; STM32; Zhang-Suen thinning algorithm

引言

移动机器人在工业、物流、医疗等领域的重要性凸显[1]。路径规划是移动机器人核心技术,也是研究领域的重点方向之一。机器人作业的环境非常复杂,自主移动是它完成各种任务的基础,行业里对路径规划算法的要求不断提高。在这种背景下,研究怎样让机器人运行得省电、高效、准点,优化路径规划方案,既有理论价值,又有实际意义[2]。

在矿道、管廊、仓储巷道这种狭窄空间中运行,对移动机器人的能力要求高。传统循迹方式比如磁导航、色带导航,得专门铺路径,成本高且不灵活。而纯视觉的方案容易受光照和灰尘影响。二维激光雷达不会受环境光的干扰,能直接收集环境轮廓,所以在巷道导航里使用得越来越频繁[3]。二维激光雷达一般是用非接触式激光测距,为实现稳定工作和精确定位,主要靠激光发射模块和光学接收模块这两个关键部件来实现[4]。雷达先发射激光,经目标反射后由接收端接收,再测算出距离[5]。

按路径信息的不同,路径规划可以分为全局规划和局部规划。全局路径规划就是机器人在有障碍的环境里,按照一种或几种指标(如路径最短),找一条从起点到终点、避免碰撞东西的最优路径。传统的全局规划算法有A*、D*、蚁群算法等[6-8]。局部路径规划,就是机器人在环境未知的情况下,仅靠传感器感知周围一小片地方,拿到局部环境信息,然后根据评价指标来避障和规划,找到去目的地的最优或者次优路径[9-10]。

结合巷道实际作业的特点来看,从离散的激光点云数据里准确、稳定地提取巷道中心线比较困难。本文借鉴图像处理里骨架化的概念,先把点云变成二值栅格图,再用Zhang-Suen细化算法提取可通行区域的中轴骨架。这个方法能在保持拓扑连通的情况下得到精确的单像素中线。控制方面采用串级双环PID结构,位置环根据横向误差算出目标航向角,角度环去跟踪该角度,这样能把横向偏差和偏航角波动分开,抑制振荡,让直线循迹更稳。


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作者信息:

许志远,侯浩天,岳斌斌,张亚坤

(黄河交通学院智能制造与电气工程学院,河南焦作454950)

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