Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture
This addresses energy constraints in agricultural robotics for improved operational efficiency, though it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of energy-aware coverage path planning for agricultural robots by proposing a reinforcement learning framework based on Soft Actor-Critic, which achieves over 90% coverage and outperforms traditional heuristic algorithms by 13.4-19.5% in coverage while reducing constraint violations by 59.9-88.3%.
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.