能力条件付きグラフ制御バリア関数による異種移動ロボットチームのゼロショット安全ナビゲーション
Capability-Conditioned Graph Control Barrier Functions for Zero-Shot Safe Navigation of Heterogeneous Mobile Robot Teams
各ロボットの状態依存の到達可能速度集合を固定次元のRadial Capability Fieldで表現し、それを条件とする共有グラフ制御バリア関数と分散方策を学習することで、未知のロボットモデルを含む異種チームでもゼロショットで安全に協調移動できる手法を提案した。
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著者: Joonkyung Kim, Janghyun Cho, Jeehyun Lee, Changjoo Nam, Yiwei Lyu
分類: cs.RO
原文アブストラクト
Multi-robot systems are increasingly deployed as heterogeneous fleets with diverse kinematic and actuation capabilities. While graph control barrier function (GCBF) methods provide a scalable framework for decentralized safe control, existing approaches typically assume homogeneous robot dynamics. Methods that directly encode robot classes can distinguish known types, but do not represent state-dependent motion capabilities or naturally transfer to unseen robot models. To address this challenge, we propose the Capability-Conditioned Heterogeneous Graph Control Barrier Function (CC-HGCBF), defined in a shared second-order reference-point operational space. This formulation provides a common geometric representation of safety across heterogeneous robots while retaining their model-specific dynamics and admissible control constraints. To enable zero-shot transfer, we introduce the Radial Capability Field (RCF), a compact, fixed-dimensional representation of each robot's state-dependent finite-horizon reachable velocity set. Conditioning the shared graph barrier and decentralized policy on the RCF enables them to adapt to changes in robot state, kinematic constraints, and control limits without relying on predefined robot identities. We jointly train the barrier and policy using proactive safety labeling and neural CBF-QP supervision, and evaluate them on mixed teams containing both seen and unseen robot models. Experiments demonstrate scalable safe coordination and zero-shot transfer to robot models and capability variations withheld during training. Physical-robot experiments further demonstrate zero-shot deployment to unseen kinematic models without retraining.