A Prescribed-Time Distributed Constrained Negotiation Allocation Algorithm for UAV Swarms
Liping Hu, Jiaqiang Zhang, Xiaolong Liang, Aiwu Yang, Ning Wang, Zhenxing Zhang · IEEE Transactions on Aerospace and Electronic Systems · 2025
To tackle the multi-constraint unbalanced target allocation challenge for UAV swarms in distributed mission scenarios, we introduce the Prescribed-time Distributed Consensusbased Target Allocation (PDC-TA) algorithm. Initially, drawing on the “capability-complexity” decomposition principle from Mosaic Warfare's system effectiveness evaluation, we reformulate the unbalanced allocation issue into a balanced allocation framework. This step lays the groundwork for a prescribed-time distributed multiconstraint target allocation model. Building on this foundation, we devise a prescribed-time distributed consensus-based target allocation protocol. This protocol integrates consensus theory with the Hungarian algorithm, effectively decoupling the distributed information negotiation from the allocation computation. This separation capitalizes on the swarm's strengths in local information interaction and parallel computation, streamlining the allocation process. Rigorous theoretical proofs affirm both the consistency and global optimality of the allocation scheme under constrained conditions. Empirical validation through balanced and unbalanced target allocation scenarios in UAV swarm operations, such as returnto-base and interception missions, underscores the algorithm's efficacy. Comparative analyses with classical distributed task allocation algorithms further highlight its superiority in runtime efficiency and solution optimality. The PDC-TA algorithm consistently delivers globally optimal and constraint-compliant target allocations within the prescribed time frame, setting it apart from traditional methods.