FLIP: 点群位置合わせによる大規模分散群ロボットのリアルタイムかつ頑健なフォーメーション計画
FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration
大規模群ロボットのフォーメーション計画を点群位置合わせ問題として定式化し、各エージェントが分散計算で最適位置を求め、外れ値除去により頑健かつ効率的な軌道計画を実現した。
詳しい要約
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著者: Yuan Zhou, Guangtong Xu, Zhenyu Hou, Jialiang Hou, Fei Gao
分類: cs.RO
原文アブストラクト
Traditional large-scale formation planning either oversimplify the formation representation which leads to poor performance, or they employ complete collaborative relationships, which results in excessive computational load. To achieve high-performance and large-scale formation planning, we transform the Optimal Formation Position Sequence \cite{c1} (OFPS) calculation problem into a spatiotemporal Point Cloud Registration (PCR) problem. Each agent derives its OFPS by distributively computing the matching result between current positions and the desired formation positions of all other agents. Then each agent optimizes the cooperative formation trajectory by using OFPS. We leverage the PCR method with outlier rejection to rapidly perform large-scale formation position registration. This prevents suboptimal trajectories and failed agents from propagating through the cooperative network and affecting more agents. Consequently, we uniformly achieve resilient, efficient, and distributed trajectory planning for large-scale swarms. The effectiveness and the superiority of the proposed method are demonstrated through large-scale simulations of 120-drone formation, and rigorous benchmarking against state-of-the-art (SOTA) methods.
