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Multi-Agent Belief Sharing through Autonomous Hierarchical Multi-Level Clustering
arXiv - CS - Multiagent Systems Pub Date : 2021-07-21 , DOI: arxiv-2107.09973 Mirco Theile, Jonathan Ponniah, Or Dantsker, Marco Caccamo
arXiv - CS - Multiagent Systems Pub Date : 2021-07-21 , DOI: arxiv-2107.09973 Mirco Theile, Jonathan Ponniah, Or Dantsker, Marco Caccamo
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.
更新日期：2021-07-22
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