HiPAN: 不整地3D環境における四足ロボットの階層的姿勢適応ナビゲーション
HiPAN: Hierarchical Posture-Adaptive Navigation for Quadruped Robots in Unstructured 3D Environments
四足ロボットの不整地3D環境でのナビゲーションのため、高レベル方策が平面速度と身体姿勢を生成し、低レベル姿勢適応制御器が実行する階層的フレームワークを提案。経路誘導カリキュラム学習で長期的ナビゲーションを実現し、シミュレーションと実機で有効性を検証した。
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著者: Jeil Jeong, Minsung Yoon, Seokryun Choi, Heechan Shin, Taegeun Yang, Sung-eui Yoon
分類: cs.RO
原文アブストラクト
Navigating quadruped robots in unstructured 3D environments poses significant challenges, requiring goal-directed motion, effective exploration to escape from local minima, and posture adaptation to traverse narrow, height-constrained spaces. Conventional approaches employ a sequential mapping-planning pipeline but suffer from accumulated perception errors and high computational overhead, restricting their applicability on resource-constrained platforms. To address these challenges, we propose Hierarchical Posture-Adaptive Navigation (HiPAN), a framework that operates directly on onboard depth images at deployment. HiPAN adopts a hierarchical design: a high-level policy generates strategic navigation commands (planar velocity and body posture), which are executed by a low-level, posture-adaptive locomotion controller. To mitigate myopic behaviors and facilitate long-horizon navigation, we introduce Path-Guided Curriculum Learning, which progressively extends the navigation horizon from reactive obstacle avoidance to strategic navigation. In simulation, HiPAN achieves higher navigation success rates and greater path efficiency than classical reactive planners and end-to-end baselines, while real-world experiments further validate its applicability across diverse, unstructured 3D environments.
