Proactive mission-time-efficient coverage path planning using hierarchical heuristics

Ensuring efficient and reliable autonomous coverage in large-scale environments remains a persistent challenge, particularly owing to the battery limitations of robotic systems. To address this challenge, this study proposes a novel, proactive energy-aware coverage path planning (CPP) framework that considers traveling and charging durations in a unified manner. The proposed method explicitly models realistic battery dynamics, including nonlinear charging and discharging behaviors. To render the problem practically solvable, it is decomposed into a hierarchical two-stage structure. Each stage is addressed using a well-suited heuristic: Ant Colony Optimization (ACO) for generating coverage paths, and a Genetic Algorithm (GA) for scheduling recharging actions. In contrast to conventional reactive approaches that respond only after the battery level becomes critical, the proposed method schedules recharging actions in advance, aiming to reduce the overall mission time proactively and strategically. Extensive simulations in synthetic, real-world-acquired, and real-world-based obstacle-rich coverage environments validate the effectiveness of the proposed method. The results demonstrate a mission time reduction of up to 24.66 %, with consistent improvements in energy reliability across varying charging station densities. These findings highlight the practicality of the proposed method as a global scheduler for real-world deployment in energy-constrained environments. Furthermore, this framework lays the foundation for extensions to multi-robot systems, enabling scalable, adaptive, and mission-time-efficient coordination in large-scale autonomous missions.