Energy-Aware Embedded Intelligence for Smart Robotic Applications

Abstract

The rapid evolution of intelligent robotic systems has increased the demand for embedded computing architectures capable of performing complex perception, decision-making, and control operations under strict energy constraints. Traditional robotic platforms often rely on centralized cloud computing or high-performance processors, resulting in increased communication latency, energy consumption, and reduced autonomy. Energy-aware embedded intelligence overcomes these drawbacks by directly embedding lightweight artificial intelligence (AI) algorithms, hardware accelerators, adaptive power management and edge-based decision-making capabilities into robotic platforms. We propose an energy-smart embedded intelligence framework for a smart robotic application, engaging the ensemble of (i) embedded AI (ii) sensor fusion mechanism (iii), Nature-mimicry based energy optimization strategy and (iv) real-time autonomous control. We have designed robotics based on dynamic workload scheduling and intelligent resource allocation for dynamically balancing the computational performance with power consumption. The paper introduces the results of an analysis on state-of-the-art in embedded intelligence methods, short description of research gaps and also insight regarding energy-efficient architectures effectiveness that addresses mobile robots, autonomous robotic systems, and more broadly industrial robots. Comparative evaluation shows that energy-aware embedded intelligence can drastically reduce energy consumption and maintain high accuracy and responsiveness. It suggests that enhanced edge-intelligence architectures will drive sustainable, autonomous, and scalable operation of next-generation robotic systems in Industry 5.0 settings.