Urban airspace multi-unmanned aerial vehicle task allocation method based on task structure perception

By constructing maps in urban airspace and introducing heuristic information based on task structure awareness, combined with ant colony optimization algorithm and dual pheromone matrix, the problem of inaccurate and inefficient task allocation in existing technologies is solved, achieving efficient multi-UAV task allocation and improving the accuracy and efficiency of UAV task allocation in urban airspace.

CN122453031APending Publication Date: 2026-07-24NANKAI UNIV
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Patent Information

Application Number
CN202610593297.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-30
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing multi-UAV mission allocation methods are unable to accurately reflect actual flight paths and costs in urban airspace, and fail to fully utilize mission structure information. This results in a lack of differentiated priority guidance in the scheduling process, making it difficult to effectively compress the overall completion time and reduce the optimization performance and stability of the scheduling results.

Method used

We construct urban airspace maps, introduce heuristic information based on task structure awareness, and adopt a dual pheromone matrix and incremental UAV alliance construction method. Combined with ant colony optimization algorithm, we allocate tasks based on spatial proximity, critical path urgency, and topological influence to optimize task sequencing and resource allocation.

Benefits of technology

It improves the authenticity and feasibility of task allocation results, reduces the overall completion time of UAVs, enhances scheduling efficiency and the quality of task allocation schemes, avoids the combinatorial space explosion problem, and improves the efficiency of solving optimal task allocation in large-scale scenarios.

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Abstract

The invention discloses an urban airspace multi-unmanned aerial vehicle task allocation method based on task structure perception, and the method comprises the following steps: constructing an urban airspace map, and building a task-task pheromone matrix and a task-unmanned aerial vehicle pheromone matrix; constructing a directed acyclic graph according to the task preposition constraint relationship, and determining a selectable task set; according to the selectable task set, gradually constructing a task allocation sequence based on a task-task pheromone matrix and heuristic information perceived by a task structure; for each task, incremental construction of an unmanned aerial vehicle alliance is carried out according to task-unmanned aerial vehicle pheromones and heuristic information; when a group of schemes and one iteration are completed, local pheromone updating and global pheromone updating are executed respectively; and when the maximum number of iterations is met, a final task allocation scheme is obtained. According to the method, the perception capability of the task structure is improved, the problem of combined space explosion is avoided while multiple constraint conditions are met, and the efficiency of solving the optimal scheme is improved.
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