ARTIFICIAL IMMUNE NETWORK-BASED MULTI-ROBOT FORMATION PATH PLANNING WITH OBSTACLE AVOIDANCE

Lixia Deng, Xin Ma, Jason Gu, Yibin Li, Zhigang Xu and Yafang Wang

References

  1. [1] J. Ni, X. Yang, J. Cheng, and S.X. Yang, Dynamic bioin-spired neural network for multi-robot formation control inunknown environments, International Journal of Robotics andAutomation, 30(3), 2015, 206–4217.
  2. [2] G. Rishwaraj, S.G. Ponnambalam, and R.M. Kuppan Chetty,Multi-robot formation control using a hybrid posture estimationstrategy, International Journal of Robotics and Automation,29(4), 2014, 206–4078.
  3. [3] Y.-T. Wang and Y.-C. Chen, Multiple-obstacle avoidance inrole assignment of formation control, International Journal ofRobotics and Automation, 27(2), 2012, 206–3430.
  4. [4] A. Noormohammadi Asl, M.B. Menhaj, and A. Sajedin, Controlof leader-follower formation and path planning of mobile robotsusing Asexual Reproduction Optimization (ARO), Applied SoftComputing, 14, 2014, 563–576.
  5. [5] L. Consolini, F. Morbidi, D. Prattichizzo, and M. Tosques,Leader-follower formation control of nonholonomic mobilerobots with input constraints, Automatica, 44(5), 2008,1343–1349.
  6. [6] Debabrata Atta and Bidyadhar Subudhi, Decentralized for-mation control of multiple autonomous underwater vehicles,International Journal of Robotics and Automation, 28(4), 2013,206–3613.
  7. [7] L. Yang, Z. Cao, and M. Tan, Dynamic formation control formultiple robots in uncertain environments, Robot, 32(2), 2010,283–288.
  8. [8] R. K. Chetty, M. Singaperumal, and T. Nagarajan, Distributedformation planning and navigation framework for wheeledmobile robots, Journal of Applied Sciences, 11(9), 2011, 1501–1509.
  9. [9] Y. Dai, and S.-G. Lee, The leader-follower formation control ofnonholonomic mobile robots, International Journal of Control,Automation and Systems, 10(2), 2012, 350–361.
  10. [10] T.-T. Yang, Z.-Y. Liu, H. Chen, and R. Pei, Formationcontrol and obstacle avoidance for multiple mobile robots, ActaAutomatica Sinica, 34(5), 2008, 588–593.
  11. [11] T. Zhang, X. Li, Y. Zhu, and S. Chen, et al., Formation andobstacle avoidance in the unknown environment of multi-robotsystem, Proceedings of the IEEE International Conference onRobotics and Biomimetics, 2009, 729–734.
  12. [12] J.V. Gomez, A. Lumbier, S. Garrido, and L. Moreno, Plan-ning robot formations with fast marching square includinguncertainty conditions, Robotics and Autonomous Systems, 61,2012, 137–152.
  13. [13] S. Liu, D. Sun, and C. Zhu, A dynamic priority based pathplanning for cooperation of multiple mobile robots in formationforming, Robotics and Computer-Integrated Manufacturing,30(6), 2014, 589–596.
  14. [14] Y. Dai and S.-G. Lee, Formation control of mobile robots withobstacle avoidance based on GOACM using onboard sensors,International Journal of Control, Automation, and Systems,12(5), 2014, 1077–1089.
  15. [15] G.-C. Luh and W. Liu, An immunological approach to mobilerobot reactive navigation, Applied Soft Computing, 8(1), 2008,30–45.
  16. [16] L. Deng, X. Ma, J. Gu, and Y. Li, Mobile robot path planningusing polyclonal-based artificial immune network, Journal ofControl Science and Engineering, 2013, 2013, 1–13.
  17. [17] L. Deng, X. Ma, J. Gu, and Y. Li, Planning multi-robot for-mation with improved poly-clonal artificial immune algorithm,International Conference on Robotics and Biomimetics, 2013,982–987.
  18. [18] L. Deng, X. Ma, J. Gu, and Y. Li, Multi-robot dynamicFormation path planning with improved polyclobal artificialimmune algorithm, Control and Intelligent Systems, 4(42),2014, 1–8.
  19. [19] L. Deng, X. Ma, J. Gu, and Y. Li, Improved poly-clonal artifi-cial immune network for multi-robot dynamic path planning,International Conference on Information and Automation,2013, 128–133.
  20. [20] Y.-Y. Min and Y.-G. Liu, Barbalat Lemma and its applica-tion in analysis of system stability [J], Journal of ShandongUniversity (engineering science), 1(1), 2007, 1–6.

Important Links:

Go Back