Task Allocation and Trajectory Optimization for Multi‐UAV Cargo Systems with Cellular‐Connected Constraints

This paper investigates a multi‐UAV cargo delivery scenario, where each UAV picks up goods from one location and delivers them to another destination while maintaining connectivity with the ground cellular network. Optimizing task assignment and UAV trajectory design to minimize completion time unde...

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Vydané v:IET communications Ročník 19; číslo 1
Hlavní autori: Zhang, Borui, Huang, Kui, Chen, Yujing, Yang, Dingcheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Stevenage John Wiley & Sons, Inc 01.01.2025
Predmet:
Ant colony optimization
Cargo
Collaboration
Communication
Completion time
Connectivity
Constraints
Deep learning
Dijkstra's algorithm
Efficiency
Energy consumption
Environmental conditions
Genetic algorithms
Investigations
Logistics
Planning
Route optimization
Route planning
Shortest-path problems
Trajectory optimization
Unmanned aerial vehicles
ISSN:1751-8628, 1751-8636
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Shrnutí:This paper investigates a multi‐UAV cargo delivery scenario, where each UAV picks up goods from one location and delivers them to another destination while maintaining connectivity with the ground cellular network. Optimizing task assignment and UAV trajectory design to minimize completion time under the constraints is a significant challenge. To address this, the approach is structured into two principal phases. First, Dijkstra's algorithm is utilized to derive the shortest paths between points while ensuring communication connectivity meets specific quality constraints. Second, these paths are integrated with a novel hybrid optimization algorithm fusing a genetic algorithm and an ant colony algorithm to solve the coupled task assignment and route planning problem subject to communication and payload limitations. The hybrid approach efficiently balances exploration and exploitation, leading to superior task allocation and route planning. Numerical results show that our proposed method is effective in balancing task allocation and reducing overall completion time by comparing it with other integrated optimization techniques.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.70106