基于结构持久图和视觉定位的多机器人编队生成与控制  

Generation and Control of Multi-robot Formation based on Structural Persistence

作　　者：刘彤 宗群 刘朋浩 董琦 LIU Tong;ZONG Qun;LIU Penghao;DONG Qi(School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072, China;China Academic of Electronics and Information Technology, Beijing 100041, China)

机构地区：[1]天津大学电气自动化与信息工程学院学院,天津300072 [2]中国电子科技集团电子科学研究院,北京100041

出　　处：《信息与控制》2018年第3期314-323,共10页Information and Control

基　　金：中国博士后科学基金资助项目(2017M620858)

摘　　要：针对结构持久图在双轮机器人编队及无人机与多机器人编队中的应用及视觉SLAM(simultaneous localization and mapping)算法在定位中的应用,研究了将最小持久编队生成与势场函数控制相结合的编队方法,采用视觉SLAM算法为机器人提供精确的位置信息.首先,基于改进的有向增加顶点操作生成最小持久编队队形;其次,提出了一种基于RGB-D相机的视觉SLAM算法,得出机器人的精确位置;再次,对队形中的多机器人分别设计势场函数,考虑双轮机器人的非完整性,提出了一种结合势场函数的双轮机器人编队控制方法,控制过程中使用的位置信息由视觉SLAM算法提供;然后,结合无人机与多机器人,实现了不同平面下多机器人的最小持久编队;最后,利用Gazebo仿真平台验证了所提理论的有效性.To apply the structural persistence graph in the formation of dual-wheel robots,unmanned aerial vehicles（ UAVs）,and multi-robot systems,as well as the visual simultaneous location and mapping（ SLAM） algorithm for positioning,we study the formation method,which combines minimally persistent formation with potential field control. The SLAM algorithm provides robots with accurate position information. First,minimally persistent formation is generated based on the improved directed increasing vertex operations. Secondly,we propose a visual SLAM algorithm based on an RGB-D camera to obtain the robot＇s exact position. Thirdly,we design a potential field function for multi-robots in the formation. Then,we propose a two-wheeled robot formation control method based on the potential field function that considers the non-integrality of two-wheeled robots. The position information used in the control process is determined by the visual SLAM algorithm.Then,we realize the minimally persistent formation of multi-robots in different planes combined with UAVs and multi-robot systems. Lastly,we verify the validity of the proposed method on the Gazebo simulation platform.

关 键 词：最小持久编队 视觉SLAM定位 势场函数 非完整性 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]