An Agent-related Asynchronous Consensus Method for Fast Scheduling of UAV Swarm

UAV swarm needs careful task and time arrangement to complete complex tasks with spatiotemporal constraints such as search and rescue, which requires distributed scheduling methods. Market-based approach is the optimal choice to satisfy the centerless and self-organized characteristics of the swarm. However, the consensus methods adopted by most market-based algorithms require synchronous communication, which takes time to wait, so scholars resort to asynchronous consensus methods. The existing asynchronous method's communication traffic increases with the number of tasks, and the total number of messages sent can be further reduced. Therefore, this paper proposes a new asynchronous method to further reduce the communication traffic and the number of messages sent required by the market-based approach. Firstly, the timestamp of when a UAV updates its information is used cleverly, which reduces the communication traffic of the original timestamp of when a task's information is updated. And the new timestamp contains more information about the non-winner that is absent in the original timestamp, which facilitates new asynchronous consensus rules to resolve task conflicts faster. Secondly, agent-related asynchronous consensus rules with new timestamps are designed to resolve task conflicts, which effectively solves the problem of information arriving out of order and potentially reduces the number of messages sent. Finally, through a self-developed ad-hoc network simulation system, the swarm scheduling under real networking conditions is simulated. A large number of Monte Carlo simulation experiments show that compared with the most representative asynchronous method Asynchronous Consensus-Based Bundle Algorithm (ACBBA), the number of messages sent and communication traffic can be reduced by 61% and 70% at most, and the scheduling time can be reduced by up to 60%.