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Dynamic formation control for autonomous underwater vehicles

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Abstract

Path planning and formation structure forming are two of the most important problems for autonomous underwater vehicles (AUVs) to collaborate with each other. In this work, a dynamic formation model was proposed, in which several algorithms were developed for the complex underwater environment. Dimension changeable particle swarm algorithm was used to find an optimized path by dynamically adjusting the number and the distribution of the path nodes. Position relationship based obstacle avoidance algorithm was designed to detour along the edges of obstacles. Virtual potential point based formation-keeping algorithm was employed by incorporating dynamic strategies which were decided by the current states of the formation. The virtual potential point was used to keep the formation structure when the AUV or the formation was deviated. Simulation results show that an optimal path can be dynamically planned with fewer path nodes and smaller fitness, even with a concave obstacle. It has been also proven that different formation-keeping strategies can be adaptively selected and the formation can change its structure in a narrow area and restore back after passing the obstacle.

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References

  1. YANG Tian-tian, SU Zhi-bao, LIU Jin, MENG Hong. Formation control and obstacle avoidance for multiple mobile robots [J]. Computer Simulation, 2011, 29(9): 215–218.

    Google Scholar 

  2. ZHAO Tao, LIU Ming-yong, ZHOU Liang-rong. A survey of autonomous underwater vehicle recent advances and future challenges [J]. Fire Control and Command Control, 2010, 35(6): 1–6. (in Chinese)

    Google Scholar 

  3. WANG Jun. Improved double populations genetic algorithm based on path planning for AUV [J]. Control Theory and Applications, 2010, 29(6): 13–16. (in Chinese)

    Google Scholar 

  4. ALVAREZ A, CAITI A, ONKEN R. Evolutionary path planning for autonomous underwater vehicles in a variable ocean [J]. IEEE J Ocean Engineering, 2004, 29(2): 418–429.

    Article  Google Scholar 

  5. YU S C, TAMAKI U. A system of multi-AUV interlinked with a smart cable for autonomous inspection of underwater structures [J]. International Journal of Offshore and Polar Engineering, 2004, 14(4): 264–272.

    Google Scholar 

  6. YANG Er-fu, GU Dong-bing. Nonlinear formation keeping and mooring control of multiple autonomous underwater vehicles [J]. IEEE/ASME Transactions on Mechatronics, 2007, 2(2): 164–178.

    Article  MathSciNet  Google Scholar 

  7. RAO A S, GEORGEFF M P. The semantics of intention maintenance for rational agents [C]// Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. California, 1995: 704–710.

    Google Scholar 

  8. LONG Sheng, PAN Ya-jun, GONG Xiang. Consensus formation control for a class of networked multiple mobile robot systems [J]. Journal of Control Science and Engineering, 2012 (2012): 1–12.

    Google Scholar 

  9. BHATTACHARYA S, LIKHACHEV M, KUMAR V. Topological constraints in search-based robot path planning [J]. Auton Robot, 2012(33): 273–290.

    Google Scholar 

  10. YANG Ao-lei, WASIF N, GEORGE W I, LI Kang. Novel decentralised formation control for unmanned vehicles [C]// 2012 Intelligent Vehicles Symposium. Spain, 2012: 13–18.

    Chapter  Google Scholar 

  11. ESSGHAIER A, BEJI L, EL KAMEL M A, ABICHOU A. Co-leaders and a flexible virtual structure based formation motion control [J]. Vehicle Autonomous Systems, 2011 (9): 108–125.

    Google Scholar 

  12. YANG Xi-xin, TANG Gong-you, LI Yang, WANG Pei-dong. Formation control for multiple autonomous agents based on virtual leader structure [C]// 24th Chinese Control and Decision Conference (CCDC). Taiyuan, China, 2012: 2833–2837.

    Google Scholar 

  13. LIU Yang, JIA Ying-min. Formation control of discrete-time multi-agent systems by iterative learning approach [J]. International Journal of Control, Automation, and Systems, 2012, 10(5): 913–919.

    Article  Google Scholar 

  14. CHEN Jian, SUN Dong, YANG Jie, CHEN Hao-yao. Leader-follower formation control of multiple non-holonomic mobile robots incorporating a receding-horizon scheme [J]. The International Journal of Robotics Research, 2010 (29): 727–747.

    Google Scholar 

  15. SADOWSKA A, KOSTÍC D, van de WOUW N, HUIJBERTS H, NIJMEIJER H. Distributed formation control of unicycle robots [C]// 2012 IEEE International Conference on Robotics and Automation (ICRA). Saint Paul, USA, 2012: 1564–1569.

    Chapter  Google Scholar 

  16. GAO Lei, YAN Jing. Formation and obstacle avoidance algorithm for multi-agent systems [J]. Journal of Hunan University of Science and Technology: Natural Science Edition, 2010, 25(3): 72–76. (in Chinese)

    Google Scholar 

  17. MAO Yu-feng, PANG Yong-jie, WANG Zhao-li. Underwater vehicle’s long voyage path planning in complex sea condition [C]// Intelligent Computing and Intelligent Systems of IEEE Int’l Conf. Shanghai, China, 2009: 661–665.

    Google Scholar 

  18. ZHU Hong-guo, ZHENG Chang-wen, HU Xiao-hui. Path planner for unmanned aerial vehicles based on modified PSO algorithm [C]// Information and Automation of ICIA. Changsha, China, 2008: 541–544.

    Google Scholar 

  19. CHEN Yang-quan, WANG Zhong-min. Formation control: A review and a new consideration [C]// Intelligent Robots and Systems of 2005 IEEE/RSJ International Conference. Edmonton, Alta, 2005: 3181–3186.

    Chapter  Google Scholar 

  20. TANNER H G, PAPPAS G J, VIJAY K. Leader-to-formation stability [J]. Robotics and Automation Society, 2004, 20(3): 443–455.

    Article  Google Scholar 

  21. DAS A K, FIERRO R, KUMAR V, OSTROWSKI J P, SPLETZER J, TAYLOR C J. A vision-based formation control framework [J]. IEEE Transactions, 2002, 18(5): 813–825.

    Google Scholar 

  22. WANG Jing, NIAN Xiao-hong, WANG Hai-bo. Consensus and formation control of discrete-time multi-agent systems [J]. Journal of Central South University of Technology, 2011, 18(4): 1161–1168.

    Article  Google Scholar 

  23. WANG Jia, WU Xiao-bei, XU Zhi-liang. A new formation control method based on potential function for multi-agent [J]. Information and control, 2008, 37(3): 263–268. (in Chinese)

    MathSciNet  Google Scholar 

  24. LONG Wang, SHI Hong, CHU Tian-guang. Flocking control of groups of mobile autonomous Agents via local feedback [C]// Proceedings of the 2005 IEEE International Symposium on Intelligent Control. Limassol, Cyprus, 2005: 441–446.

    Google Scholar 

  25. ZHANG Min, SHEN Yi, WANG Qiang, WANG Yi-bo. Dynamic artificial potential field based multi-robot formation control [C]// Instrumentation and Measurement Technology Conference (I2MTC). Austin, USA, 2010: 1530–1534.

    Google Scholar 

  26. LIU Chang-an, YAN Xiao-hu, LIU Chun-yang, WU Hua. Dynamic path planning for mobile robot based on improved ant colony optimization algorithm [J]. Chinese Journal of Electronics, 2011, 39(5): 1220–1224. (in Chinese)

    Google Scholar 

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Correspondence to Xue-feng Yan  (燕雪峰).

Additional information

Foundation item: Project(NS2013091) supported by the Basis Research Fund of Nanjing University of Aeronautics and Astronautics, China

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Yan, Xf., Gu, F., Song, C. et al. Dynamic formation control for autonomous underwater vehicles. J. Cent. South Univ. 21, 113–123 (2014). https://doi.org/10.1007/s11771-014-1922-7

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  • DOI: https://doi.org/10.1007/s11771-014-1922-7

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