用强化学习让农用机器人边省电边全覆盖
Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture
- 基于SAC强化学习，融合CNN与LSTM动态规划路径
- 覆盖率达90%以上，比传统算法高13.4%-19.5%
- 适合大田作业中需续航优化的农业机器人
覆盖路径规划（CPP）是农业机器人的重要能力；然而现有方法常忽视能量限制，导致在大规模或资源受限环境中任务中断。本文提出一种基于软演员-评论家（SAC）强化学习的能量感知CPP框架，适用于含障碍物和充电站的网格环境。为实现能量约束下的鲁棒自适应决策，框架融合卷积神经网络（CNN）进行空间特征提取，以及长短期记忆网络（LSTM）捕捉时间动态。设计专用奖励函数，联合优化覆盖率、能耗与返航约束。实验表明，该方法始终实现超过90%的覆盖率，且在覆盖率上优于RRT、PSO和ACO等启发式算法13.4%-19.5%，约束违反率降低59.9%-88.3%。结果验证了SAC框架在农业机器人能量受限场景下的有效性与可扩展性。
原文摘要 · Abstract (English)
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.
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