Coordination in multi-agent systems is challenging for agile robots such as unmanned aerial vehicles (UAVs), where relative agent positions frequently change due to unconstrained movement. The problem is exacerbated through the individual take-off and landing of agents for battery recharging leading to a varying number of active agents throughout the whole mission. This work proposes autonomous hierarchical multi-level clustering (MLC), which forms a clustering hierarchy utilizing decentralized methods. Through periodic cluster maintenance executed by cluster-heads, stable multi-level clustering is achieved. The resulting hierarchy is used as a backbone to solve the communication problem for locally-interactive applications such as UAV tracking problems. Using observation aggregation, compression, and dissemination, agents share local observations throughout the hierarchy, giving every agent a total system belief with spatially dependent resolution and freshness. Extensive simulations show that MLC yields a stable cluster hierarchy under different motion patterns and that the proposed belief sharing is highly applicable in wildfire front monitoring scenarios.
翻译:多试剂系统的协调对无人驾驶飞行器(无人驾驶飞行器)等灵活机器人来说具有挑战性,因为无人驾驶飞行器(无人驾驶飞行器)的相对代理位置由于不受限制的移动而经常发生变化,这一问题因电池充电的代理器个别起飞和着陆而加剧,导致整个特派团内不同数目的活跃物剂出现。这项工作提议采用分散的方法,将多层次的自主集群(MLC)形成一个集群等级结构。通过由集束头执行的定期集群维护,实现了稳定的多层次集群。由此产生的等级制被用作解决当地交互应用(如无人驾驶飞行器跟踪问题)的通信问题的骨干。利用观测汇总、压缩和传播,代理器在整个层级共享当地观测,使每个代理器有一个完全依赖空间的分辨率和新鲜度的系统信念。广泛的模拟表明,在不同的运动模式下,刚果解放运动产生稳定的集群等级,拟议的信仰共享在野火前监测情景中非常适用。