AgniNav: 構成駆動型クロスエンボディローカルナビゲーション
AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation
ロボットの衝突エンベロープを4パラメータで定義し、単眼画像から疑似レーザースキャンを生成するネットワークとプランナーを構成することで、再学習なしで車輪型・脚型・人型ロボットに適用可能なナビゲーション手法を提案した。
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著者: Tianhao Zang, Siwei Cheng, Haidong Huang, Shanze Wang, Wei Zhang
分類: cs.RO
原文アブストラクト
Monocular local navigation is attractive for lightweight robots, but existing vision-based policies often couple perception to a specific body, camera height, and footprint, making transfer from wheeled bases to legged platforms dependent on retraining or active depth hardware. This paper introduces AgniNav, a configuration-driven local navigation framework that standardizes cross-embodiment transfer at the collision-envelope level. Each robot is specified by a measurable four-parameter safety envelope: collision-relevant height, front length, rear length, and half width. The height parameter conditions an image-to-scan network to predict a one-dimensional, collision-relevant pseudo-laserscan from a monocular color image, while the remaining footprint parameters configure a dimension-aware local planner for collision checking. Training uses height-conditioned column-minimum scan labels generated from paired color-depth data, allowing the same image to supervise different safety envelopes without collecting robot-specific data. To the best of our knowledge, AgniNav is the first monocular local-navigation framework that jointly conditions perception and planning on a shared collision-envelope configuration for zero-retraining deployment across wheeled, quadruped, and humanoid platforms. Real-robot experiments on a Turtlebot2, Unitree Go2, and Accelerated Evolution K1 achieve 39/40, 18/20, and 18/20 successes with 0/40, 1/20, and 2/20 collisions, respectively, while running at 30 Hz on Jetson Orin.
