摘要
针对目前安防巡逻机器人系统中对机器人主控机负载能力要求较高的问题,设计一种基于机器人操作系统(robot operating system,ROS)的分布式安防巡逻机器人系统。将同时定位与建图(simultaneous localization and mapping,SLAM)节点以及视觉识别节点分布于局部ROS网络中的机器人主控机和远程监控端,SLAM方面采用gmapping算法实现动态环境中的地图构建以及机器人的定位;视觉识别方面采用TensorFlow深度学习框架,利用TensorFlow Object Detection API实现场景里多个物体的识别。结果表明,该系统为基于ROS的分布式安防巡逻机器人系统的设计提供了参考。
Aiming at the problem that the current security patrol robot system has a high requirement for the load capacity of the robot master controller,a distributed security patrol robot system based on robot operating system(ROS)is designed.The simultaneous Localization and mapping(SLAM)nodes and the vision recognition nodes are distributed in the robot host and the remote monitor in the local ROS network.On the SLAM side,the gmapping algorithm is used to realize the mapping in the dynamic environment and the localization of the robot;In the aspect of visual recognition,TensorFlow deep learning framework is used to recognize multiple objects in the scene by using TensorFlow Object Detection API.The results show that the system provides a reference for the design of ROS-based distributed security patrol robot system.
作者
丁林祥
陶卫军
黄潇
Ding Linxiang;Tao Weijun;Huang Xiao(Chengxian College,Southeast University,Nanjing 210088,China;School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《兵工自动化》
北大核心
2024年第6期87-90,共4页
Ordnance Industry Automation
基金
2021年度东南大学成贤学院青年教师科研发展基金项目(z0024)。
