融合大脑认知与高精度定位，构建更智能的导航系统。
A Preliminary Exploration of the Differences and Conjunction of Traditional PNT and Brain-inspired PNT
- 对比传统定位、生物脑定位与仿脑定位的差异
- 提出四层融合框架，结合精准计算与类脑智能
- 为未来类脑导航发展提供路线图，适合智能系统研究者
实现通用定位、导航与授时（PNT）是我们的长期目标。复杂环境对PNT提出了更高要求：更强的鲁棒性、更低的能耗以及更强的认知能力。本文探讨如何在无人系统中赋予类脑空间认知导航能力，并结合机器化PNT的高精度，推动通用PNT的发展。我们提出从“工具导向”向“认知驱动”转变的新视角与路线图。主要贡献包括：(1) 对传统PNT、生物脑PNT与类脑PNT进行多层级差异分析；(2) 构建观察-能力-决策-硬件四层融合框架，实现数值精度与类脑智能的统一；(3) 提出未来类脑PNT发展的前瞻性建议。
原文摘要 · Abstract (English)
Developing universal Positioning, Navigation, and Timing (PNT) is our enduring goal. Today's complex environments demand PNT that is more resilient, energy-efficient and cognitively capable. This paper asks how we can endow unmanned systems with brain-inspired spatial cognition navigation while exploiting the high precision of machine PNT to advance universal PNT. We provide a new perspective and roadmap for shifting PNT from "tool-oriented" to "cognition-driven". Contributions: (1) multi-level dissection of differences among traditional PNT, biological brain PNT and brain-inspired PNT; (2) a four-layer (observation-capability-decision-hardware) fusion framework that unites numerical precision and brain-inspired intelligence; (3) forward-looking recommendations for future development of brain-inspired PNT.
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