基于改进RTAB-Map算法的爬壁机器人导航研究  

Research on navigation of wall-climbing robot based on improved RTAB-Map algorithm

作　　者：覃超 唐东林[1] 游东潘 丁超 饶胜 何媛媛 QIN Chao;TANG Donglin;YOU Dongpan;DING Chao;RAO Sheng;HE Yuanyuan(School of Mechanical and Electrical Engineering,Southwest Petroleum University,Chengdu 610500,China;School of Intelligent Manufacturing,Chengdu Technology University,Chengdu 611730,China;Sichuan Special Equipment Inspection Institute,Chengdu 610061,China)

机构地区：[1]西南石油大学机电工程学院,四川成都610500 [2]成都工业学院智能制造学院,四川成都611730 [3]四川省特种设备检验研究院,四川成都610061

出　　处：《工程设计学报》2025年第1期32-41,共10页Chinese Journal of Engineering Design

基　　金：国家市场监督管理总局科技计划项目(2022MK115);四川省重点研发计划项目(2002ZYZFGY04);南充市-西南石油大学市校科技战略合作项目(23XNSYSX0048)。

摘　　要：针对爬壁机器人难以对大型石化装备壁面感知和自动化检测等难题,提出了一种改进RTAB-Map(real-time appearance-based mapping,基于外观的实时建图)算法,通过多传感器数据融合实现爬壁机器人定位、建图与导航。首先,搭建了具有壁面吸附能力的机器人运动底盘,以保证爬壁机器人在壁面灵活稳定运动;其次,针对爬壁机器人在壁面滑移导致的里程计累计误差问题,采用扩展卡尔曼滤波并融合编码器和惯性测量单元的数据,为建图和导航提供精确的里程计信息;再次,基于RTAB-Map算法对深度相机、激光雷达及里程计的数据进行融合,生成二维栅格和三维点云地图,实现对装备壁面的完整描述,并基于融合数据构建了爬壁机器人导航算法的框架;最后,在装备壁面进行了实验验证。结果表明:采用融合里程计算法,能显著减小航向角误差,航向角平均误差为0.78°,相对轮式里程计规划,航向角误差减少了88.94%;改进RTAB-Map算法提高了爬壁机器人在壁面环境下的建图与感知能力,结合路径规划算法,实现了机器人自主导航。研究结果对爬壁机器人自动化检测技术的研究及应用具有一定的参考意义。Aiming at the difficulty of wall-climbing robot in wall sensing and automated detection of large petrochemical equipment,an improved RTAB-Map(real-time appearance-based mapping)algorithm was proposed to realize the wall-climbing robot's localization,mapping and navigation by fusing multiple sensor data.Firstly,a robot motion chassis with wall adsorption ability was built to ensure the flexible and stable movement of the wall-climbing robot on the wall surface.Secondly,to address the cumulative error of the odometer resulting from the wall-climbing robot's slippage on the wall surface,the extended Kalman filter was utilized to fuse the data of encoder and inertial measurement unit to provide accurate odometer information for mapping and navigation.Thirdly,based on the RTAB-Map algorithm,the data of depth camera,LiDAR and odometer were fused to generate 2D grid and 3D point cloud map to realize a complete description of the equipment wall,and the navigation algorithm framework of the wall-climbing robot was constructed based on the fusion data.Finally,the experimental validation was carried out on the equipment wall.The results showed that the yaw angle error could be significantly reduced by using fusion mileage method,the average error of yaw angle was reduced by 88.94%compared with that under wheel odometer planning with an average error of 0.78°.The improved RTAB-Map algorithm improved the wall-climbing robot's mapping and sensing ability in the wall environment,and realized the autonomous navigation combined with the path planning algorithm.The research results have a certain reference significance for the research and application of automated detection technology of wall-climbing robots.

关 键 词：爬壁机器人 RTAB-Map(基于外观的实时建图)算法 扩展卡尔曼滤波 多传感器融合 导航 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]