AI RESEARCH
TinyNav: End-to-End TinyML for Real-Time Autonomous Navigation on Microcontrollers
arXiv CS.LG
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ArXi:2603.11071v1 Announce Type: cross 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 TinyNa, 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.