Generation and Control of Multi-robot Formation based on Structural Persistence
LIU Tong1, ZONG Qun1, LIU Penghao1, DONG Qi2
1. School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072, China;
2. China Academic of Electronics and Information Technology, Beijing 100041, China
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.
Abstract：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.
刘彤, 宗群, 刘朋浩, 董琦. 基于结构持久图和视觉定位的多机器人编队生成与控制[J]. 信息与控制, 2018, 47(3): 314-323.
LIU Tong, ZONG Qun, LIU Penghao, DONG Qi. Generation and Control of Multi-robot Formation based on Structural Persistence. Information and control, 2018, 47(3): 314-323.
王佳, 吴晓蓓, 徐志良. 一种基于势能函数的多智能体编队控制新方法[J]. 信息与控制, 2008, 37(3):263-268. Wang J, Wu X P, Xu Z L. A new formation control method based on potential function for multi-agent[J]. Information and Control, 2008, 37(3):263-268.
[14]
Endres F, Hess J, Sturm J, et al. 3-D mapping with an RGB-D camera[J]. IEEE Transactions on Robotics, 2014, 30(1):177-187.
[13]
Strasdat H, Montiel J M M, Davison A J. Visual SLAM:Why filter[J]. Image and Vision Computing, 2012, 30(2):65-77.
[2]
Zhang Z, Zhang R, Liu X. Multi-robot formation control based on behavior[C]//International Conference on Computer Science and Software Engineering. Piscataway, NJ, USA:IEEE, 2008:1045-1048.
[8]
罗小元, 邵士凯, 关新平, 等. 多智能体最优持久编队动态生成与控制[J]. 自动化学报, 2013, 39(9):1431-1438. Luo X Y, Shao S K, Guan X P, et al. Dynamic generation and control of optimally persistent formation for multi-agent systems[J]. Acta Automatica Sinica, 2013, 39(9):1431-1438.
[11]
Ramazani S, Selmic R R, Queiroz M D. Chapter thirteen-Multiagent layered formation control based on rigid graph theory[M]//Control of Complex Systems. Amsterdam, Netherlands:Elsevier, 2016:397-419.
[7]
Hendrickx J M, Fidan B, Yu C, et al. Primitive operations for the construction and reorganization of minimally persistent formations[J]. Bibliogr, 2006, 113(3):246-253.
[16]
Rublee E, Rabaud V, Konolige K, et al. ORB:An efficient alternative to SIFT or SURF[C]//IEEE International Conference on Computer Vision. Piscataway, NJ, USA:IEEE, 2012:2564-2571.
[1]
Sun T, Liu F, Pei H, et al. Observer-based adaptive leader-following formation control for non-holonomic mobile robots[J]. IET Control Theory & Applications, 2012, 6(18):2835-2841.
[3]
Lei C, Ma B. A nonlinear formation control of wheeled mobile robots with virtual structure approach[C]//Chinese Control Conference. Piscataway, NJ, USA:IEEE, 2015:1080-1085.
[4]
Hendrickx J M, Anderson B, Delvenne J C, et al. Directed graphs for the analysis of rigidity and persistence in autonomous agent systems[J]. International Journal of Robust and Nonlinear Control, 2007, 17(10/11):960-981.
[5]
Yu C, Hendrickx J M, Fidan B, et al. Three and higher dimensional autonomous formations:Rigidity, persistence and structural persistence[J]. Automatica, 2007, 43(3):387-402.
[6]
Hendrickx J M, Fidan B, Yu C, et al. Formation reorganization by primitive operations on directed graphs[J]. IEEE Transactions on Automatic Control, 2008, 53(4):968-979.
[10]
Ramazani S, Selmic R R, De Queiroz M. Non-planar multi-agent formation control using coning graphs[C]//IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, NJ, USA:IEEE, 2014:3091-3096.
[12]
Connelly R, Whiteley W J. Global rigidity:The effect of coning[J]. Discrete & Computational Geometry, 2010, 43(4):717-735.
[15]
Galvez-López D, Tardos J D. Bags of binary words for fast place recognition in image sequences[J]. IEEE Transactions on Robotics, 2012, 28(5):1188-1197.
[17]
Kümmerle R, Grisetti G, Strasdat H, et al. G2o:A general framework for graph optimization[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA:IEEE, 2011:3607-3613.
[18]
Mur-Artal R, Tardós J D. ORB-SLAM2:An open-source SLAM system for monocular, stereo, and RGB-D cameras[J]. IEEE Transactions on Robotics, 2016, 33(5):1255-1262.
[19]
Muja M, Lowe D G. Scalable nearest neighbor algorithms for high dimensional data[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 36(11):2227-2240.
[20]
刘春, 宗群, 窦立谦. 基于持久图的双轮机器人编队生成与控制[J]. 控制工程, 2017, 24(3):518-523. Liu C, Zong Q, Dou L Q. Generation and control of wheeled robots formation based on persistent graph theory[J]. Control Engineering, 2017, 24(3):518-523.
[21]
Endres F, Hess J, Engelhard N, et al. An evaluation of the RGB-D SLAM system[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA:IEEE, 2012:1691-1696.
