摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的详细数据已经公布。根据NewsRx记者从泰国曼谷发回的新闻报道，研究称，"两种类型的传感器，即光探测和测距G(LiDAR)和全球导航卫星系统的实时运动学与初始导航系统(RTK-GNSS/INS)，被用于户外移动机器人的定位，但发现单独使用激光雷达和RTK-GNSS/INS不足以实现精确定位"。这项研究的财政支持来自泰国曼谷国王蒙古t的拉德克拉邦理工学院工程学院。新闻记者引用了蒙库特国王理工学院的一篇研究文章,为提高传感器的可靠性,提出了一种基于自适应网络模糊推理系统(ANFIS)的传感器融合方法。收集了两个传感器的数据，以创建一个用于ANFIS训练的数据库T。结果表明，从这两个传感器的融合中得出的模型提供的结果更接近于单独使用每个传感器获得的数值。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news reporting originating in Bangkok, Thailand, by NewsRx journalists, research stated, "Two types of sensors, light detection and rangin g (LiDAR) and real-time kinematic of global navigation satellite system with ine rtial navigation system (RTK-GNSS/INS), are used for the localization of outdoor mobile robots. However, using LiDAR and RTK-GNSS/INS independently was found to be insufficient for achieving precise positioning." Financial support for this research came from School of Engineering, King Mongku t's Institute of Technology Ladkrabang, Bangkok, Thailand. The news reporters obtained a quote from the research from the King Mongkut's In stitute of Technology, "Therefore, a sensor fusion approach based on an adaptive -network-based fuzzy inference system (ANFIS) was implemented to enhance reliabi lity. In this research, data from both sensors were collected to create a datase t for training with ANFIS. The findings indicated that the model derived from th e fusion of these two sensors provided results that were much closer to the actu al values obtained using each sensor independently."
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