Multi-UAV United Task Allocation via Extended Market Mechanism Based on Flight Path Cost

In response to the challenge of effectively allocating united tasks for multiple unmanned aerial vehicles (UAVs) in complex environments, this paper proposes an extended market mechanism task allocation method for multi-UAV united tasks under obstacle constraints. In the task information pre-construction phase, the multi-layer task lists and bidding information are constructed. In the UAV task information extension phase, by employing UAV Dubins paths to calculate flight costs and benefits, the task list and bidding information of a UAV are extended based on the greedy principle. Subsequently, in the conflict resolution phase, UAVs receive bidding information and reconstruct bidding information to achieve the consistency of allocation result. Finally, the corresponding Dubins paths are subjected to cubic B-spline optimization based on the allocation results to generate real flight paths for UAVs. Simulation experiments demonstrate the effectiveness of the proposed method in addressing the issue of united task allocation for multi-UAV in complex environments.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/icus61736.2024.10840003
Citations
3
Primary Topic
Optimization and Search Problems
Type
article
Field-Weighted Citation Impact
1.05
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article

Multi-UAV United Task Allocation via Extended Market Mechanism Based on Flight Path Cost

Zexu Zhang, Jinlong Liu, Tianlai Xu
3 citations
Optimization and Search Problems
1.05
article

Multi-UAV United Task Allocation via Extended Market Mechanism Based on Flight Path Cost

Zexu Zhang, Jinlong Liu, Tianlai Xu
article en
3 citations

Abstract

In response to the challenge of effectively allocating united tasks for multiple unmanned aerial vehicles (UAVs) in complex environments, this paper proposes an extended market mechanism task allocation method for multi-UAV united tasks under obstacle constraints. In the task information pre-construction phase, the multi-layer task lists and bidding information are constructed. In the UAV task information extension phase, by employing UAV Dubins paths to calculate flight costs and benefits, the task list and bidding information of a UAV are extended based on the greedy principle. Subsequently, in the conflict resolution phase, UAVs receive bidding information and reconstruct bidding information to achieve the consistency of allocation result. Finally, the corresponding Dubins paths are subjected to cubic B-spline optimization based on the allocation results to generate real flight paths for UAVs. Simulation experiments demonstrate the effectiveness of the proposed method in addressing the issue of united task allocation for multi-UAV in complex environments.

Harbin Institute of Technology (CN)
Openalex Percentile: Top 20%
Optimization and Search Problems
1.05
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