Edge AI for Real-Time Robotic Systems: Architectures, Deployment Strategies, and Performance Optimization

The deployment of artificial intelligence on edge devices has emerged as a critical enabler for real-time robotic systems, where latency constraints and computational efficiency directly impact system performance and safety. This paper presents a comprehensive analysis of edge AI architectures and deployment strategies for robotic applications, examining the interplay between hardware platforms, model optimization techniques, and application-specific requirements. We systematically evaluate the performance characteristics of modern edge computing platforms, including the NVIDIA Jetson family, and assess optimization techniques such as quantization, pruning, and inference acceleration frameworks. Drawing from established patterns in modular robotic architectures and closed-loop control systems, we propose a five-stage deployment methodology that guides practitioners through requirement analysis, model selection, optimization, hardware alignment, and system integration. Experimental evaluation demonstrates that optimized edge deployments achieve inference latencies below 10 milliseconds—an order of magnitude improvement over cloud-based processing—while maintaining accuracy within 2% of full-precision baselines. The findings provide actionable guidance for robotics practitioners seeking to deploy AI capabilities on resource-constrained embedded platforms while meeting stringent real-time requirements.