# Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization
## 1 Introduction  
UAV swarms represent a pivotal research and development direction within the realm of intelligent unmanned systems [1, 2, 3]. Such swarms, comprised of multiple UAVs operating in synchrony, function akin to parallel systems in distributed computing, markedly enhancing mission execution efficiency and expanding operational scope. In comparison to solitary UAV operations, swarm systems demonstrate superior fault tolerance, particularly against single-point failures, thereby augmenting overall robustness. As a highly complex system, the UAV swarm encompasses various aspects such as networking communication [4, 5, 6, 7, 8], low-level control strategies [9, 10, 11], and top-level mission planning [12, 13, 14], with each aspect posing significant challenges in research and implementation. Task allocation technology constitutes a core component of top-level mission planning, dictating the sequence of tasks that UAVs should undertake [15, 16, 17]. For instance, in logistics distribution, the manner in which UAV swarms distribute parcel delivery tasks to ensure timely delivery to designated locations before deadlines exemplifies this technology's application. Analogous application scenarios encompass search and rescue, intelligent transportation systems, regional monitoring, and so on [18, 19, 20]. The tasks referred to in this paper are those with spatio-temporal constraints, specifically, tasks requiring UAVs to arrive at designated locations and commence operations before a specified deadline. Given that various tasks possess differing task values, and these values decrease over time, the initial timing of UAVs executing these tasks will impact the overall benefit derived from the completion of the task set by the UAV swarm. Improving the benefit oftask completion within a UAV swarm leads to the optimization of resource distribution and enhances the adaptability and responsiveness of the overall system [21; 22; 23]. Generally, task allocation technology can be categorized into centralized and distributed approaches. In the former, a central node takes charge of allocating all tasks, while other UAVs execute the assigned tasks they receive. Conversely, the latter operates without a central node, where each UAV is responsible for its task selection. The centralized approach excels in handling task constraints and optimizing allocation schemes [24; 25]. However, its drawbacks are equally evident: the aggregation of situational information at a single point and the reliance on the core node for computing allocation schemes lead to issues such as heavy communication burdens, a sharp increase in computational load with swarm size, and vulnerability to single-point failures [26; 27; 28]. In contrast, the distributed approach, while less adept at managing inter-task correlations and achieving performance optimization in task allocation compared to the centralized method, effectively addresses the issues of central node dependency and computational explosion. It boosts superior flexibility and robustness [29; 30; 31]. Auction-based methods represent a significant approach for distributed resolution of task allocation problems in UAV swarms [32; 33; 34]. Their rapid solution capability makes them well-suited to meet the real-time requirements of in-flight planning for UAV swarms. This approach optimizes swarm task allocation through a two-phase model: individual task planning followed by swarm negotiation for allocation. In the initial phase of individual task planning, each UAV constructs its task plan based on locally available information, ensuring adherence to both UAV and task constraints during this process. Subsequently, in the swarm negotiation phase, all UAVs within the swarm exchange their task plan information. In cases where task conflicts arise, conflict resolution mechanisms are employed. Ultimately, through iterative cycles of these two phases, an optimized and conflict-free task allocation scheme is produced [35; 36; 37].