在微控制器上实现实时自主导航，模型仅23000参数
TinyNav: End-to-End TinyML for Real-Time Autonomous Navigation on Microcontrollers
- 用20帧深度数据滑动窗口输入，量化2D卷积网络直接输出控制指令
- 23000参数模型推理延迟仅30毫秒，支持实时响应
- 适合资源受限的低成本机器人开发，无需外部计算设备
自主导航通常依赖高功耗处理器，限制了低成本机器人的应用。尽管微控制器提供资源高效替代方案，但对模型复杂度有严格约束。我们提出TinyNav，一个端到端的TinyML系统，可在ESP32微控制器上实现实时自主导航。通过自定义训练并量化的二维卷积神经网络，处理20帧深度数据滑动窗口，预测转向与油门指令。通过避免3D卷积和循环层，23000参数模型实现30毫秒推理延迟。相关性分析与Grad-CAM验证表明模型具备稳定的空间感知与避障行为。TinyNav证明了响应式自主控制可直接部署于高度受限的边缘设备，减少对外部计算资源的依赖。
原文摘要 · Abstract (English)
Autonomous navigation typically relies on power-intensive processors, limiting accessibility in low-cost robotics. Although microcontrollers offer a resource-efficient alternative, they impose strict constraints on model complexity. We present TinyNav, an end-to-end TinyML system for real-time autonomous navigation on an ESP32 microcontroller. A custom-trained, quantized 2D convolutional neural network processes a 20-frame sliding window of depth data to predict steering and throttle commands. By avoiding 3D convolutions and recurrent layers, the 23k-parameter model achieves 30 ms inference latency. Correlation analysis and Grad-CAM validation indicate consistent spatial awareness and obstacle avoidance behavior. TinyNav demonstrates that responsive autonomous control can be deployed directly on highly constrained edge devices, reducing reliance on external compute resources.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。