協調マルチロボットナビゲーションのためのサブチーム化と適応的フォーメーション制御
Subteaming and Adaptive Formation Control for Coordinated Multi-Robot Navigation
ロボットチームが狭い通路などで動的にサブチームに分かれ、隊形を保ちながら協調移動できる階層型学習フレームワークSTAFを提案した。
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著者: Zihao Deng, Peng Gao, Williard Joshua Jose, Maggie Wigness, John Rogers, Brian Reily, Christopher Reardon, Hao Zhang
分類: cs.RO
原文アブストラクト
Coordinated multi-robot navigation is essential for robots to operate as a team in diverse environments. During navigation, robot teams usually need to maintain specific formations, such as circular formations to protect human teammates at the center. However, in complex scenarios such as narrow corridors, rigidly preserving predefined formations can become infeasible. Therefore, robot teams must be capable of dynamically splitting into smaller subteams and adaptively controlling the subteams to navigate through such scenarios while preserving formations. To enable this capability, we introduce a novel method for SubTeaming and Adaptive Formation (STAF), which is built upon a unified hierarchical learning framework: (1) high-level deep graph cut for team splitting, (2) intermediate-level graph learning for facilitating coordinated navigation among subteams, and (3) low-level policy learning for controlling individual mobile robots to reach their goal positions while avoiding collisions. To evaluate STAF, we conducted extensive experiments in both indoor and outdoor environments using robotics simulations and physical robot teams. Experimental results show that STAF enables the novel capability for subteaming and adaptive formation control, and achieves promising performance in coordinated multi-robot navigation through challenging scenarios. More details are available on the project website: https://hcrlab.gitlab.io/project/STAF.
