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.

翻译：单目局部导航对轻量级机器人具有吸引力，但现有基于视觉的策略常将感知耦合到特定机体、相机高度和占地面积，导致从轮式基座向足式平台的迁移依赖重新训练或主动深度硬件。本文提出AgniNav——一种在碰撞包络层面标准化跨实体迁移的配置驱动局部导航框架。每个机器人由可测量的四参数安全包络定义：碰撞相关高度、前向长度、后向长度及半宽。高度参数约束图像-扫描网络从单目彩色图像预测一维碰撞相关伪激光扫描，剩余占地面积参数配置维度感知局部规划器进行碰撞检测。训练使用配对颜色-深度数据生成的受高度约束的列最小值扫描标签，使同一图像可监督不同安全包络而无需收集机器人特定数据。据我们所知，AgniNav是首个在共享碰撞包络配置上联合约束感知与规划的单目局部导航框架，实现轮式、四足及人形平台零重训练部署。在Turtlebot2、Unitree Go2及Accelerated Evolution K1上的真实机器人实验中，分别达成39/40、18/20和18/20次成功，伴随0/40、1/20和2/20次碰撞，并在Jetson Orin上以30Hz频率运行。