Real-Time Embedded AI Framework for Autonomous Robotic Decision-Making

Abstract

Autonomous robotic systems require sophisticated artificial intelligence capabilities while operating under stringent embedded computing constraints, including limited processing power, memory, and energy resources. This article introduces the Real-Time Embedded AI Framework (RE-AIF), a novel hybrid architecture that successfully integrates deterministic real-time control with adaptive AI inference within resource-constrained embedded platforms. The framework implements a three-layer hierarchical design separating perception, cognition, and execution functions while maintaining tight integration and temporal coordination across all system components. RE-AIF utilizes a strategic hybrid programming methodology combining C++ for deterministic real-time control with Python for flexible AI inference, achieving performance characteristics unattainable through single-language implementations. The architecture incorporates comprehensive sustainability features including dynamic voltage scaling, energy hibernation strategies, and memory optimization techniques that address the complete spectrum of embedded AI deployment challenges. Performance optimization strategies encompass lightweight AI model deployment through quantized convolutional neural networks, hardware acceleration utilization, and compile-time optimization techniques that maximize embedded system capabilities. Real-time orchestration mechanisms implement priority-based task scheduling, dynamic workload reassignment protocols, and predictive task mapping for power management, enabling sustained autonomous operation. Industrial applications demonstrate the framework effectiveness in precision assembly systems, continuous inspection operations, and cycle time enhancement while defense applications validate capabilities in autonomous navigation, swarm coordination, and sovereign autonomy scenarios. The implementation framework provides practical integration examples, including hybrid execution flows, neural network deployment, and real-time control loop implementations that demonstrate the viability of embedded AI deployment in mission-critical autonomous systems.