Autonomous planetary exploration demands real-time, high-fidelity environmental perception. Standard deep learning models require massive computational resources. Conversely, space-qualified onboard computers operate under strict power, thermal, and memory limits. This disparity creates a severe engineering bottleneck, preventing the deployment of highly capable perception architectures on extraterrestrial exploration platforms. In this foundational concept paper, we propose the theoretical architecture for the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys) to resolve this bottleneck. We present a mathematical blueprint integrating an INT8 Quantized Neural Network (QNN) designed specifically for Quantization Aware Training (QAT). To address sensor fragility, we mathematically formalize an Adaptive Multi-Sensor Fusion (AMF) module. By deriving the exact integer requantization multiplier required for spatial attention gating, this module actively selects and fuses Optical Imagery (OI) and Digital Elevation Models (DEMs) at the feature level, ensuring reliable perception during extreme cross-illuminations and optical hardware dropouts. Furthermore, the architecture introduces anchor-free, center-to-edge regression heads, protected by a localized FP16 coordinate conversion, to accurately frame asymmetrical lunar craters without catastrophic integer truncation. Rather than presenting physical hardware telemetry, this manuscript establishes the theoretical bounds, structural logic, and mathematical justifications for the architecture. We outline a rigorous Hardware-in-the-Loop (HITL) evaluation protocol to define the exact testing criteria required for future empirical validation, paving the way for next-generation space-mission software design.

翻译：自主行星探测需要实时、高保真的环境感知。标准深度学习模型需要巨大的计算资源。相反，航天级星载计算机在严格的功率、热力学和内存限制下运行。这种差异造成了严重的工程瓶颈，阻碍了高能力感知架构在地外探测平台上的部署。在这篇基础概念论文中，我们提出了自适应量化行星撞击坑检测系统 (AQ-PCDSys) 的理论架构以解决这一瓶颈。我们提出了一种数学蓝图，集成了专为量化感知训练 (QAT) 设计的INT8量化神经网络 (QNN)。为解决传感器脆弱性，我们以数学形式形式化了一个自适应多传感器融合 (AMF) 模块。通过推导空间注意力门控所需的精确整数重量化乘数，该模块在特征层面主动选择并融合光学图像 (OI) 和数字高程模型 (DEM)，确保在极端交叉光照和光学硬件丢失期间的可靠感知。此外，该架构引入了无锚框、中心到边缘的回归头，并辅以局部FP16坐标转换，以精确框定不对称的月球撞击坑，同时避免灾难性的整数截断。本手稿并未呈现物理硬件遥测数据，而是建立了该架构的理论边界、结构逻辑和数学依据。我们概述了一个严格的硬件在环 (HITL) 评估协议，以定义未来经验验证所需的确切测试标准，为下一代太空任务软件设计铺平道路。