一种用于水电站水工建筑物缺陷检测的水下机器人系统设计

Design of an Underwater Robot System for Detecting Defects in Hydraulic Structures at Hydropower Stations

水电站水工建筑物缺陷检测目前是由观察型机器人或潜水员完成,存在检测手段少、运行受水流影响等问题。对此,文章针对水电站水工建筑物结构及缺陷特点,研制了一套水下检测机器人系统。该系统利用水动力仿真、静水力分析及稳性分析等方法,优化推力和布局布置、外形等选型和设计,解决了本体浮游和履带复合控制模式、不规则坝面吸附技术、多自由度姿态翻转和运动、缺陷位置定位等技术问题;同时,采用自主开发后处理软件,对声光电传感器数据进行融合,完成了缺陷判别与测量以及修补材料方量的估算,软件识别理论误差为7.4%,小于项目的20%误差率要求,解决了缺陷判别与测量和修补材料方量的估算问题,最终实现了机器人在水电站水工建筑物的水平面、斜面坡、直立面及曲面环境下的水下缺陷检查、缺陷定位、缺陷尺寸测量及数据记录收集等水下无人化检测作业的目标。最后,通过水池功能试验,验证了该水下检测机器人系统的可行性。

Current defect detection techniques for hydraulic structures at hydropower stations are conducted by inspection robots or divers,facing challenges such as limited inspection methods and the impact of water currents.In response to this,the paper developes an underwater robot system for inspection tasks based on the structural and defect characteristics of hydraulic buildings in hydropower stations.Using methodologies such as hydrodynamic simulation,and hydrostatic analysis and stability analysis,the system optimizes propulsion and layout structure,shape selection and design,solving problems inherent to body floating and caterpillar track hybrid control modes,adhesive technology for irregular dam surfaces,multi-degree of freedom attitude flipping,movement,and precise location of defects.Furthermore,independently developed post-processing software is used to consolidate data from acousto-optic-electric sensors,ensuring identification of defects,measurement,and estimation of repair material quantities,the theoretical error of software recognition is 7.4%,which is less than the 20%error rate required by the project.This solution overcomes the challenges of defect measurement and estimation of required materials for repairs.Ultimately,the objective is to facilitate autonomous underwater operations including defect inspections,locating defects,dimension measurements,and data acquisition for robots operating on flat,sloping,upright,and curved surfaces of hydraulic structures in hydropower stations.Finally,pool functional test validations have confirmed the feasibility of the proposed underwater detection robot system.