Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 04032
Number of page(s) 5
Section Circuit Simulation, Electric Modules and Displacement Sensor
DOI https://doi.org/10.1051/matecconf/201823204032
Published online 19 November 2018
  1. LAWTON J R, YOUNG B J, BEARD R W. A decentralized approach to elementary formation maneuvers [C] //Proc of IEEE International Conference on Robotics & Automation. Piscataway, NJ: IEEE Press, 2000: 2728-2733. [Google Scholar]
  2. LUO Xiao-yuan, HAN Na-ni, GUAN Xin-ping. Leader-following consensus protocols for formation control of multi-agent network [J] . Journal of Systems Engineering and Electronics, 2011, 22(6): 991-997. [CrossRef] [Google Scholar]
  3. Balch T, Arkin R C. Behavior-based Formation Control for Multi-robot Teams[J]. IEEE Transactions on Robotics & Automation, 1998, 14(6): 926-939. [CrossRef] [Google Scholar]
  4. Wang G, Li D, Gan W, et al. Study on Formation Control of Multi-Robot Systems[M]. 2013. [Google Scholar]
  5. Oh K K, Park M C, Ahn H S. A survey of multi-agent formation control[M]. Pergamon Press, Inc. 2015. [Google Scholar]
  6. Zhang Y, Zeng L, Li Y, et al. Multi-robot formation control using leader-follower for MANET[C]// IEEE International Conference on Robotics and Biomimetics. IEEE, 2010:337-342. [Google Scholar]
  7. Yu W, Chen G, Cao M. Distributed leader–follower flocking control for multi-agent dynamical systems with time-varying velocities[J]. Systems & Control Letters, 2010, 59(9): 543-552. [CrossRef] [Google Scholar]
  8. Chen J, Sun D, Yang J, et al. Leader-Follower Formation Control of Multiple Non-holonomic Mobile Robots Incorporating a Receding-horizon Scheme[J]. International Journal of Robotics Research, 2010, 29(6): 727-747. [CrossRef] [Google Scholar]
  9. Ghamry K A, Zhang Y. Formation control of multiple quadrotors based on leader-follower method[C]// International Conference on Unmanned Aircraft Systems. IEEE, 2015:1037-1042. [Google Scholar]
  10. Zhen-Zhong Y U, Yan J H, Zhao J, et al. Mobile robot path planning based on improved artificial potential field method[J]. Journal of Harbin Institute of Technology, 2011, 43(1): 349-354. [Google Scholar]
  11. Chen Y B, Luo GC, MeiY S, et al. UAV path planning using artificial potential field method updated by optimal control theory[J]. International Journal of Systems Science, 2016, 47(6): 1407-1420. [CrossRef] [Google Scholar]
  12. Zhou L, Li W. Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning[C]// Seventh International Symposium on Computational Intelligence and Design. IEEE, 2015:429-432. [Google Scholar]
  13. Wu L, Hui W. Multi-Robot Formation Control and Simulation[C]// Control and Decision Conference. IEEE, 2013:2830-2833. [Google Scholar]
  14. Bishop A N, Deghat M, Anderson B D O, et al. Distributed formation control with relaxed motion requirements[J]. International Journal of Robust & Nonlinear Control, 2015, 25(17): 3210-3230. [CrossRef] [Google Scholar]
  15. Lorenzen M, Belabbas M A. Distributed local stabilization in formation control[C]// Control Conference. IEEE, 2014:2914-2919. [Google Scholar]
  16. Li F, Ding Y, Hao K. A Dynamic Leader-Follower Strategy for Multi-robot Systems[C]// IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2016:298-303. [Google Scholar]
  17. Laird J E. Soar cognitive architecture[J]. 2012. [Google Scholar]

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