Coverage Path Planning (CPP) is a fundamental capability for agricultural robots; however, existing solutions often overlook energy constraints, resulting in incomplete operations in large-scale or resource-limited environments. This paper proposes an energy-aware CPP framework grounded in Soft Actor-Critic (SAC) reinforcement learning, designed for grid-based environments with obstacles and charging stations. To enable robust and adaptive decision-making under energy limitations, the framework integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dynamics. A dedicated reward function is designed to jointly optimize coverage efficiency, energy consumption, and return-to-base constraints. Experimental results demonstrate that the proposed approach consistently achieves over 90% coverage while ensuring energy safety, outperforming traditional heuristic algorithms such as Rapidly-exploring Random Tree (RRT), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) baselines by 13.4-19.5% in coverage and reducing constraint violations by 59.9-88.3%. These findings validate the proposed SAC-based framework as an effective and scalable solution for energy-constrained CPP in agricultural robotics.

翻译：覆盖路径规划（CPP）是农业机器人的一项基本能力；然而，现有解决方案常常忽视能量约束，导致在大规模或资源受限环境中操作无法完成。本文提出了一种基于柔性演员-评论家（SAC）强化学习的能量感知CPP框架，专为包含障碍物和充电站的栅格化环境设计。为了在能量限制下实现鲁棒且自适应的决策，该框架集成了卷积神经网络（CNN）用于空间特征提取，以及长短期记忆（LSTM）网络用于时序动态建模。设计了一个专门的奖励函数，以联合优化覆盖效率、能量消耗和返回基地约束。实验结果表明，所提出的方法在确保能量安全的同时，持续实现超过90%的覆盖率，在覆盖率上优于快速探索随机树（RRT）、粒子群优化（PSO）和蚁群优化（ACO）等传统启发式算法基线13.4-19.5%，并将约束违反减少了59.9-88.3%。这些发现验证了所提出的基于SAC的框架是农业机器人学中能量受限CPP问题的一种有效且可扩展的解决方案。