Summary:本文提出的框架从机器人导航的物理基础出发，在自动驾驶的决策架构中找到了它最具现实压力的应用场景。具体方法是将决策依据明确分割为物理层与语义层：前者为可通行介质框架（Traversable Medium Framework，TMF），由表面法向角 θ、摩擦系数 μ、介质阻力 R 三个物理量构建；后者涵盖规则、社会规范与舒适性等来自人类社会的语义信息。二者通过二值掩码乘法（Binary Mask Multiplication，BMM）顺序执行，每一个决定均可追溯至其所在层级。本论文会在近期被更新，以补充新的研究成果。

The Traversable Medium Framework: Navigation Unified

The framework bridges this gap using first-principles physics. By centering navigation on surface angle and friction, we collapse two distinct research pipelines into one continuous, robot-agnostic model. From legged robots to AGVs, this conceptual unification offers a high-efficiency, mathematically elegant tool for navigating everything from urban structures to unstructured terrain. This is Article #1; its companion, Article #2, is linked above.

Summary: Once we define the robot's perception world into a physical layer and semantic layer, their relationship becomes a simple multiplication, and sensor conflict dissolves — not because we found a better way to adjudicate between radar and vision, but because we defined what each one is for.

This is Article #2; its companion, Article #1, is linked below.