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Research on a Task Planning Method for Multi-Ship Cooperative Driving

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Abstract

A new method for a cooperative multi-task allocation problem (CMTAP) is proposed in this paper, taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperative driving (MCD) system. On the basis of the general CMTAP model, an MCD task assignment model is established. Furthermore, a genetic ant colony hybrid algorithm (GACHA) is proposed for this model using constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This algorithm uses a genetic algorithm (GA) based on a task sequence, while the crossover and mutation operators are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony algorithm (ACA) is used to produce the initial population. In order to meet the environmental constraints of ship navigation, the results of the task allocation and path planning are combined to generate an MCD task planning scheme. The results of a simulated experiment using simulated data show that the proposed method can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship navigation environment constraints. Moreover, the experimental results using real data also indicate that the proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Yaojie Chen  (陈姚节), Shanshan Xiang  (项珊珊) & Feixiang Chen  (陈飞翔)

Authors
  1. Yaojie Chen  (陈姚节)
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  2. Shanshan Xiang  (项珊珊)
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  3. Feixiang Chen  (陈飞翔)
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Corresponding author

Correspondence to Yaojie Chen  (陈姚节).

Additional information

Foundation item: the National Science and Technology Support Program (No. 2015BAG20B05)

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Chen, Y., Xiang, S. & Chen, F. Research on a Task Planning Method for Multi-Ship Cooperative Driving. J. Shanghai Jiaotong Univ. (Sci.) 24, 233–242 (2019). https://doi.org/10.1007/s12204-019-2057-7

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  • DOI: https://doi.org/10.1007/s12204-019-2057-7

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