An integrated tiny-YOLO v3 and Q-iteration framework for stable, energy-efficient autonomous navigation of quadruped robots on AMB82-mini microcontrollers.

The deployment of advanced artificial intelligence, specifically deep learning (DL) for perception and reinforcement learning (RL) for control, on ultra-low-power, microcontroller-based quadruped robots presents significant challenges. A system-level engineering approach is presented, integrating these computationally intensive methodologies. Central to this is an object detection module, powered by a lightweight Deep Neural Network (DNN), specifically a Tiny-YOLOv3 model, running on an AMB82-Mini microcontroller. The robot’s perception capabilities are provided by the AMB82-Mini, while its real-time locomotion control system is implemented on a Teensy 4.0 microcontroller. This integration leverages meticulous optimization techniques, including INT8 quantization and efficient TFLite Micro deployment. The object detection module achieves approximately 7.8 frames per second (128.32 ms inference latency), enabling robust obstacle avoidance and stable locomotion. Experimental validation was primarily conducted using the custom-built TMUBot quadruped robot, demonstrating its capabilities across diverse terrains. The results underscore the potential of using machine learning with low-power microcontrollers to achieve complex control schemes for small-scale robotic applications.