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Research on a hybrid neural network task assignment algorithm for solving multi-constraint heterogeneous autonomous

Jingyu Ru1, Dongqiang Hao1, Xiangyue Zhang1

  • 1Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China.

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|January 27, 2023
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Summary

This study introduces a novel Fast Graph Pointer Network (FGPN) for efficient multi-constrained task assignment in heterogeneous autonomous underwater vehicle (AUV) clusters. The FGPN method improves assignment efficiency while maintaining accuracy for underwater exploration tasks.

Keywords:
cluster collaborationgenetic algorithmgraph pointer networkmultiple autonomous underwater robotstask assignment problem

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Operations Research

Background:

  • Underwater exploration relies on multi-robot task assignment, crucial for military, fishery, and energy sectors.
  • Existing heuristic methods struggle with optimal task assignment in heterogeneous autonomous underwater vehicle (AUV) clusters, especially with autonomous decision-making.
  • The complexity of multi-constrained detection task assignment for AUV clusters necessitates advanced algorithmic solutions.

Purpose of the Study:

  • To propose a novel Fast Graph Pointer Network (FGPN) method for efficient and accurate multi-constrained task assignment in heterogeneous AUV clusters.
  • To enhance the efficiency of task assignment for detection/communication AUV clusters while ensuring solution accuracy.
  • To address the challenge of large-scale cooperative task assignment by transforming it into a Multiple Traveling Salesman Problem (MTSP).

Main Methods:

  • A two-stage detection algorithm involving task node clustering based on communication distance.
  • A neural network model utilizing Graph Pointer Network (GPN) for local task assignment post-clustering.
  • Integration of GPN with a genetic algorithm to form the proposed Fast Graph Pointer Network (FGPN) method.
  • Formulation of a large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode.

Main Results:

  • The FGPN method demonstrates superior solution efficiency compared to traditional heuristic methods for task assignment scales of 300 to 2,000 task nodes.
  • The solution quality achieved by FGPN is comparable to that of heuristic methods.
  • Experimental validation confirms the effectiveness of the proposed FGPN algorithm for large-scale cooperative task assignment problems.

Conclusions:

  • The FGPN method offers an effective approach to solving complex, large-scale task assignment problems for heterogeneous AUV clusters.
  • The proposed method provides a valuable reference for addressing similar large-scale task assignment challenges in diverse fields.
  • This research advances autonomous decision-making capabilities in multi-robot systems for underwater applications.