Real-Time Embedded AI for Autonomous Robotics: Hybrid Control & Inference

Autonomous robotics demands advanced AI under strict embedded constraints such as limited compute, memory, and energy. This talk presents the Real-Time Embedded AI Framework (RE-AIF), a three-layer architecture that unifies deterministic real-time control with adaptive AI inference on resource-constrained systems. Built on a Perception–Cognition–Execution model, it enables synchronized sensor fusion, optimized inference, and reliable actuator control. RE-AIF combines C++ for real-time execution with Python-based AI modules via low-overhead bindings, achieving performance beyond single-language systems. It incorporates embedded optimizations such as quantized neural networks, ARM NEON/GPU acceleration, compile-time scheduling, and lock-free communication. Energy-aware techniques like dynamic voltage scaling and memory pooling ensure sustained, efficient operation. Real-world applications demonstrate improved precision, throughput, and 24/7 reliability, validating RE-AIF for mission-critical autonomous systems.