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Volume 22 Issue 4
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2014  >  22(4): 277-281
HAO Li-li, GU Hao, KANG Feng-ju, YANG Hui-zhen. A Dynamic Mission Planning Method for AUV Formation with Resource Constraint[J]. Journal of Unmanned Undersea Systems, 2014, 22(4): 277-281. doi: 10.11993/j.issn.1673-1948.2014.04.008
Citation: HAO Li-li, GU Hao, KANG Feng-ju, YANG Hui-zhen. A Dynamic Mission Planning Method for AUV Formation with Resource Constraint[J]. Journal of Unmanned Undersea Systems, 2014, 22(4): 277-281. doi: 10.11993/j.issn.1673-1948.2014.04.008
shu

A Dynamic Mission Planning Method for AUV Formation with Resource Constraint

doi: 10.11993/j.issn.1673-1948.2014.04.008
  • 1.

    School of Marine Science and Technology, Northwestern Polytechnical University, Xi′an 710072, China

  • 2.

    National Key Laboratory of Underwater Information Process and Control, Xi′an 710072, China

  • Received Date: 2014-03-18
  • Rev Recd Date: 2014-04-01
  • Publish Date: 2014-08-20
    Abstract
  • Dynamic mission planning is a key technology for coordinating the coupling among complex environment, autonomous underwater vehicle(AUV) with limited resources, and dynamic tasks to maximize the synergism of the members in AUV formation. This paper aims to propose a distributed mission planning algorithm for the AUV formation that undergoes resource constraint and operates in an unknown dynamic environment. The resource inequality function is employed, and a novel resource balance-based allocation algorithm is proposed. In combination with the objective func-tion of efficiency maximum, a contract net-based mathematical model of multi-objective optimization with multiple constraints is established. Then, the integration of formal modeling, simulation and validation is presented based on Colored Petri Nets to analyze the logic, grammar, structure and properties of a mission planning system. Simulation results show that this method can avoid the overload on the winner caused by the rule of seeking efficiency maximum, and the longer waiting time caused by load balancing strategies, thus better performance is achieved.

     

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