Improved two-stage task allocation of distributed UAV swarms based on an improved auction mechanism

Tan, Chaoren; Liu, Xin

doi:10.1007/s13042-024-02218-z

Int. J. Mach. Learn. & Cyber.

2024

DOI: 10.1007/s13042-024-02218-z

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Improved two-stage task allocation of distributed UAV swarms based on an improved auction mechanism

Chaoren Tan,

Xin Liu

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Cited by 2 publications

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Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles

Wang,

et al. 2024

Aerospace

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For the cooperative reconnaissance assignment of unmanned aerial vehicles (UAVs) on multiple targets, this paper presents a flexible combinatorial-bids-based auction (FCBA) method that can optimize the number of UAVs for each target. Considering the reconnaissance effectiveness enhancement achieved with cooperative observation and the time-critical characteristic of targets, the multitarget assignment problem is formulated as a nonlinear integer optimization to maximize the cooperative effectiveness. To achieve target assignment without predetermining the number of UAVs for each target, a combinatorial bidding framework is proposed, and an allocation method for rewards and costs among the cooperative UAVs is constructed. Strategies for auction iteration and bid updating are also designed to acquire equilibrium results under the combinatorial bidding mechanism. The simulation results show that the proposed method can generate satisfactory suboptimal results according to the enumerated solutions. A comparison of the results demonstrates that the FCBA can provide comparable optimal results to a genetic algorithm but has better computational efficiency, and the reconnaissance effectiveness can be improved by considering cooperative observation.

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Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles

Wang,

et al. 2024

Aerospace

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Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization

Chen,

Huang

et al. 2024

Drones

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The auction mechanism stands as a pivotal distributed solution approach for addressing the task allocation problem in unmanned aerial vehicle (UAV) swarms, with its rapid solution capability well-suited to meet the real-time requirements of aerial mission planning for UAV swarms. Building upon the auction mechanism, this paper proposes a distributed task allocation method for multi-UAV grounded in swarm benefits optimization. The method introduces individual benefit variation to quantify the effect of a task on the benefit of a single UAV, thereby enabling direct optimization of swarm benefit through these individual benefit variations. Within the formulated individual benefit calculation, both the spatial distance between tasks and UAVs and the initial task value along with its temporal decay are taken into account, ensuring a thorough and accurate assessment. Additionally, the method incorporates real-time updates of individual benefits for each UAV, reflecting the dynamic state of task benefit fluctuations within the swarm. Monte Carlo simulation experiments demonstrate that, for a swarm size of 16 UAVs and 80 tasks, the proposed method achieves an average swarm benefit improvement of approximately 2% and 4% over the Consensus-Based Bundle Algorithm (CBBA) and Performance Impact (PI) methods, respectively, thus validating its effectiveness.

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Improved two-stage task allocation of distributed UAV swarms based on an improved auction mechanism

Cited by 2 publications

References 31 publications

Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles

Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles

Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization

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