基于多传感器融合的物流AGV精准定位及在人机安全中的应用

Logistics AGV precise positioning based on multi-sensor fusion and its application in human-machine safety

针对物流AGV机器人定位通常依赖单一传感器,环境和数据精度问题导致定位精度、智能车间中人机协作存在不安全隐患的问题,文章提出了结合激光雷达和RGB-D摄像头进行建图和定位,再引入AR视觉标签,并设计多传感器基于扩展卡尔曼滤波定位框架,充分利用多源传感器,实现提高定位精度至6 mm。同时,智能车间中人机协作中存在不安全隐患,采用卷积神经网络VGG16进行实时不安全识别,以检测潜在安全风险,利用物流AGV的摄像头在机器人空载回程时进行安全监测,避免智能车间安全事故发生。对比传统方法,本研究实现了更精准定位和可靠的环境安全监测。

Logistics AGV robot positioning usually relies on a single sensor,leading to diminished positioning accuracy due to environmental and data precision issues,as well as safety concerns in human-machine collaboration within smart workshops.This study proposes the integration of laser radar and RGB-D cameras for mapping and positioning,further introducing AR visual tags.Additionally,a multi-sensor framework based on extended Kalman filtering is devised,leveraging multiple sensor inputs to elevate positioning accuracy to 6mm.Simultaneously,there are safety hazards present in human-machine collaboration within smart workshops.Real-time unsafe recognition using the VGG16 convolutional neural network is employed to detect potential safety risks.Moreover,the logistics AGV’s camera performs safety monitoring during the robot’s empty return journey,preventing safety incidents in smart workshops.In comparison to conventional methods,this research achieves more precise positioning and reliable environmental safety monitoring.