This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in bees and ants inspires decentralised algorithms for groups of agents to adapt to changing task demand. Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies. We address the problems of handling these challenges and improve the efficiency of the algorithm for these requirements, whilst decreasing the communication cost with a new data structure. The proposed algorithm converges to a conflict-free, feasible solution of which previous algorithms are unable to account for. Furthermore, the algorithm takes into account heterogeneous agents, deadlocking and a method to store assignments for a dynamical environment. Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks.

中文翻译：

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一种基于共识的分组算法，用于具有复杂需求的多智能体协作任务分配。

本文着眼于代理与无人机合作的共识算法。其基础是基于共识的捆绑算法，该算法被扩展为允许需要代理合作完成单个任务的多代理任务。灵感来自真社会动物的合作和改进任务的认知行为。使用在蜜蜂和蚂蚁中观察到的行为激发了代理组的分散算法以适应不断变化的任务需求。提供了进一步的扩展，以通过增加的设备要求和任务相关性来改进代理处理任务复杂性。我们解决了处理这些挑战的问题，并针对这些要求提高了算法的效率，同时使用新的数据结构降低了通信成本。所提出的算法收敛到一个无冲突、可行的解决方案，而以前的算法无法解决该问题。此外，该算法考虑了异构代理、死锁和为动态环境存储分配的方法。仿真结果表明，在多代理任务上达成共识减少了数据使用量和通信时间。