Energy-Aware Navigation in Maze-Like Environments: Balancing Exploration and Recharge Strategies in Autonomous Agents

This paper presents an energy-aware framework for autonomous agents navigating maze-like environments characterized by limited visibility and heterogeneous traversal costs. The model extends a previously established navigation framework based on visibility, memory, and exploratory tendency by integrating an internal energy regulation mechanism. Agents are equipped with a recharge threshold that governs when exploration is interrupted to restore energy, allowing them to balance goal pursuit with resource preservation. Simulations were conducted across different maze densities and recharge strategies to evaluate the effects of energy-awareness on navigation performance. The results suggest that the optimal recharge threshold is context-dependent, varying according to environmental complexity and connectivity.