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A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and

Wanqing Zhao, Qinggang Meng, Paul W H Chung

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    This study introduces a novel heuristic distributed task allocation method for cooperating multivehicle systems, optimizing assignments for search and rescue scenarios. The algorithm ensures conflict-free solutions and enhances overall system performance.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Operations Research

    Background:

    • Cooperating multivehicle systems increasingly utilize distributed task allocation.
    • Existing research often lacks systematic analysis of problems and methods.
    • Search and rescue scenarios present complex task assignment challenges.

    Purpose of the Study:

    • To systematically analyze distributed task allocation for multivehicle systems.
    • To propose a novel heuristic distributed task allocation method for multitask assignment.
    • To optimize task allocation in search and rescue scenarios.

    Main Methods:

    • Developed a general scenario description and system configuration for search and rescue.
    • Formulated the task allocation objective mathematically.
    • Proposed a heuristic distributed method with task inclusion and consensus phases.
    • Defined a 'significance' metric for tasks based on local cost contribution.

    Main Results:

    • The proposed method provides conflict-free task assignments.
    • Numerical simulations show outstanding performance compared to existing algorithms.
    • The algorithm effectively optimizes the defined mathematical objective.

    Conclusions:

    • The novel heuristic method is simple, effective, and suitable for distributed computing.
    • It addresses the need for systematic analysis in multivehicle task allocation.
    • The approach demonstrates significant performance improvements in simulated scenarios.