A Review of Intelligent Power Management and AI-Assisted Energy-Efficient Control in Robotics

As robotic platforms have become more capable, the need for improved power efficiency has grown due to increased applications and computational loads. Several methods and controllers are available in various types of robotics that can achieve increased power efficiency. This paper reviews intelligent power management methods and energy-efficient controls in untethered battery-powered robotics including dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), AI-assisted adaptive dynamic programming (DP) control systems, AI-assisted model predictive control (MPC) systems, and hybrid energy storage system (HESS) hardware well suited for multi-objective AI integration. Robotic neural networks and AI-enhancement are identified as promising directions for advanced research. However, the need to improve training power efficiency calls for further research if these AI-enhancement systems are to be integrated onboard robotic platforms. This paper provides the background and case study implementation of robotic power efficiency methods across various scales of development to illustrate the current capabilities of robotic platforms. Efficiency improvements are quantified and opportunities for advancements are presented, as well as key findings reached through this in-depth review.