Sensors and Navigation for Energy-Efficient Mobile Robots

Abstract

This study examines the role of sensor systems and navigation technologies in improving the energy efficiency of autonomous mobile robots (AMRs). The research considers LiDAR, depth cameras, IMUs, and current sensors as sources of environmental and operational data for energy-aware decision making. Energy-aware trajectory planning, velocity profile smoothing, adaptive speed control, and dynamic power management of sensors and computing resources are analyzed as key mechanisms for reducing energy consumption during motion. Particular attention is given to the integration of multisensor perception with navigation and motion planning, as well as to the trade-off between computational energy consumption and mechanical energy savings. The study shows that integrating perception, energy-aware path planning, and dynamic power management can support more efficient and sustainable operation of mobile robotic platforms.