Embedded Intelligence for Autonomous Robotics Decision-Making: A Framework for Real-Time Edge Computing in Industrial Applications

Abstract

Contemporary autonomous robotics systems face significant operational challenges due to excessive dependence on cloud-based computing infrastructure, resulting in latency issues, communication vulnerabilities, and unsustainable energy consumption patterns. This article introduces a comprehensive embedded intelligence framework that integrates real-time sensor fusion, cognitive inference, and behavior planning directly within constrained hardware environments. The framework combines deterministic control systems with lightweight machine learning inference engines, enabling robots to execute independent decisions with substantially reduced latency and enhanced safety protocols. The three-tier architectural design encompasses perception, decision, and actuation layers that collectively provide robust autonomous capabilities without external computational dependencies. Implementation utilizes optimized algorithms for energy management, AI model quantization, and workload orchestration to achieve substantial improvements in operational efficiency. The framework demonstrates successful deployment across diverse industrial applications, including warehouse automation, manufacturing robotics, defense systems, and agricultural platforms. Performance validation confirms significant reductions in power consumption and runtime improvements while eliminating cloud processing dependencies. Field testing across multiple environments validates system reliability and adaptability under challenging operational conditions. The embedded intelligence architecture establishes new benchmarks for sustainable autonomous robotics while addressing critical requirements for industrial deployment and environmental responsibility.