主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2026, Vol. 45 ›› Issue (3): 90-99.

• • 上一篇    下一篇

一种基于ROS的森林草原风力灭火机器人系统研究

任长清, 赵鑫, 杨春梅, 刘林   

  1. （东北林业大学 机电工程学院，黑龙江 哈尔滨 150040）
  • 收稿日期:2025-01-24 修回日期:2025-09-01 出版日期:2026-03-15 发布日期:2026-03-15
  • 作者简介:任长清，东北林业大学机电工程学院副教授，主要从事林业与木工机械设计研究，黑龙江省哈尔滨市香坊区和兴路26号 ，150040。
  • 基金资助:
    中央财政林业科技推广示范项目（黑［2023］TG25号）

Research on a forest and grassland pneumatic fire-fighting robot system based on ROS

Ren Changqing, Zhao Xin, Yang Chunmei, Liu Lin   

  1. (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China)
  • Received:2025-01-24 Revised:2025-09-01 Online:2026-03-15 Published:2026-03-15

摘要： 针对森林草原地表火灾燃烧迅速、范围广泛，以及在复杂环境下灭火机器人导航与火源定位的难题，设计了一种基于机器人操作系统（ROS）的森林草原风力灭火机器人系统。系统硬件部分由履带式机器人底盘、风机系统、风筒控制机构以及组合惯导、摄像头和激光雷达等多种传感器构成，采用GPS卫星定位信息与AMCL算法融合实现机器人的定位。提出一种基于模糊逻辑的动态加权A*算法，通过障碍物密度与分布离散度的模糊推理生成环境复杂度指标，在25%障碍密度的30×30栅格地图仿真试验中，与传统A*算法相比，搜索节点数下降约82.1%，规划时间缩短约52.8%，总代价降低约1.8%；在45%障碍密度的30×30栅格地图仿真试验中，与传统A*算法相比，搜索节点数减少约24.4%，总代价下降约3.4%，规划时间减少约22.3%。相较于其他算法，在DWA评价函数中引入动态障碍物影响因子能够在保证安全性的前提下，在更短时间内准确到达目标点；建立火焰高度反馈的风筒角度控制策略，能够精准定位火源并调整风筒角度进行灭火。结果表明，该系统能够在复杂林地环境中实现自主避障与目标定位，能够满足森林草原复杂环境下的实际灭火需求。

关键词: 机器人操作系统, 风力灭火机器人, A*算法, DWA算法, 路径规划

Abstract: Aiming at the rapid burning and wide range of forest and grassland surface fires, and the difficult problem of navigation and fire source localization of fire-fighting robots in complex environments, a forest and grassland pneumatic fire-fighting robotic system based on robot operating system（ROS） is designed. The hardware part of the system consists of a tracked robot chassis, fan system, wind turbine control mechanism, and a combination of inertial guidance, camera and LIDAR and other sensors, using GPS satellite positioning information and AMCL algorithm fusion to achieve the robot's localization. Proposing a fuzzy logic-based dynamic weighted A* algorithm to generate the environmental complexity index through fuzzy reasoning on the density of the obstacles and the distribution of the discrete degree in the 25% obstacle density of 30×30 raster map simulation experiments, compared with the traditional A* algorithm, the number of search nodes decreased by about 82.1%, the planning time was shortened by about 52.8%, and the total generation value was reduced by about 1.8%; in the 45% obstacle density of 30×30 raster map simulation experiments, compared with the traditional A* algorithm, the number of search nodes was reduced by about 24.4%, and the total generation value was reduced by about 3.4%, the The planning time is reduced by about 22.3%. The dynamic obstacle influence factor is introduced into the DWA evaluation function, which can accurately reach the target point in a shorter time compared with other algorithms under the premise of guaranteeing the safety; and the blower angle control strategy with flame height feedback is established, which can accurately locate the fire source and adjust the angle of the blower to extinguish the fire. The results show that the system is able to achieve autonomous obstacle avoidance and target positioning in the complex woodland environment, and can meet the demand for fire extinguishing in the complex environment of forest and grassland.

Key words: ROS, pneumatic fire-fighting robot, A* algorithm, DWA algorithm, path planning