项目来源

国家重点研发计划(NKRD)

项目主持人

刘勇

项目受资助机构

浙江大学

立项年度

2017

立项时间

未公开

项目编号

2017YFB1302003

项目级别

国家级

研究期限

未知 / 未知

受资助金额

168.00万元

学科

智能机器人

学科代码

未公开

基金类别

未公开

关键词

自主导航 ; 港口agv ; 建图 ; autonomous navigation ; AGV ; mapping

参与者

王越；尹欢

参与机构

未公开

项目标书摘要：自主移动机器人或AGV是智能技术的一个重要发展方向。目前，世界制造业的生产模式正在面临着从批量生产向用户定制的转变，对智能、个性化制造有着强劲的需求。拥有自主移动技术的机器人，必将成为未来智能制造中不可替代的重要装备和自动化手段。除了制造业以外，先进移动操作机器人技术还可广泛应用于医院、家政等服务领域，以及港口运输、载人航天、救灾等特殊领域，具有广阔发展前景。在技术层面，智能AGV的最首要任务是解决自定位。在复杂多变的作业环境下，基于GPS的定位技术无法满足导航精度，因此高精度地图是机器人定位技术的必要。本报告本报告将介绍如下几方面的贡献:\t第一章介绍面向三维激光传感器信息的地点重识别问题，该问题解决了地图构建中的闭环检测，减少地图构建的误差累积。重点介绍了基于神经网络的地点重识别方法，并与其它算法进行了对比，本课题的算法在准确性和效率上都获得了提升。\t第二章介绍面向异构地图的定位方法，该方法利用异构传感器信息之间的关联性使视觉能够在激光地图上定位，从而屏蔽视觉传感器在实际环境中存在的大量环境变化因素，从而使基于视觉的长期定位方法成为可能。该算法在室外环境中实现了时间跨度一年的定位。\t第三章为本报告的总结和结论部分，总结本报告所介绍的各方面技术及所取得的效果。

Application Abstract: Autonomous mobile robots or AGVs are an important development direction of intelligent technology.At present,the production mode of the world's manufacturing industry is facing a shift from mass production to customization,and there is a strong demand for intelligent and personalized manufacturing.Robots with autonomous mobile technology will become an irreplaceable important equipment and automation in the future of intelligent manufacturing.In addition to manufacturing,advanced mobile robot technology can also be widely used in hospitals,home economics and other service areas,as well as special areas such as port transportation,manned spaceflight,disaster relief,etc.,with broad development prospects.At the technical level,the most important task of intelligent AGV is to solve self-positioning.In the complex and varied working environment,GPS-based positioning technology can not meet the navigation accuracy,so high-precision map is necessary for robot positioning technology.This report will present the following contributions in this report:The first chapter introduces the location re-identification problem for 3D laser sensor information.This problem solves the closed-loop detection in map construction and reduces the error accumulation of map construction.The method of location re-identification based on neural network is introduced,and compared with other algorithms,the algorithm of this subject has been improved in accuracy and efficiency.The second chapter introduces the location method for heterogeneous maps,which uses the correlation between heterogeneous sensor information to enable visual positioning on the laser map,thereby shielding the large number of environmental changes that the visual sensor has in the actual environment.Make vision-based long-term positioning methods possible.The algorithm achieves a one-year positioning of time span in an outdoor environment.Chapter III is the summary and conclusion part of this report.It summarizes the various technologies and effects achieved in this report.

项目受资助省

浙江省