Energy-Aware Navigation Strategies for Autonomous Service Robots

Abstract

Autonomous Service Robots (ASRs) operating in large-scale facilities like hospitals, warehouses, and airports face strict operational challenges related to battery longevity, continuous task availability, and efficient path planning. Standard navigation algorithms typically optimize paths solely for geometric distance or transit duration, often ignoring the complex thermodynamic and mechanical energy trade-offs caused by frequent velocity changes, irregular floor surfaces, and variable cargo payloads. This paper introduces an energy-aware navigation framework that incorporates a comprehensive physical power model directly into a dynamic path-planning algorithm. By fusing environmental costmaps with real-time payload tracking and kinetic energy estimation, the proposed framework allows service platforms to minimize overall energy depletion without sacrificing time-to-target performance. Multi-scenario experimental evaluations conducted using physical mobile platforms demonstrate that this energy-conscious architecture yields substantial power savings compared to standard distance-optimal baselines. The results show that combining electrical loss metrics with structural costmaps provides the operational reliability required for true, unassisted long-term robot autonomy.