Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture

TL;DR

This paper introduces an energy-aware reinforcement learning framework using SAC, CNNs, and LSTMs for coverage path planning in agriculture, achieving high coverage and energy safety in resource-limited environments.

Contribution

It presents a novel SAC-based approach integrating CNNs and LSTMs for adaptive, energy-efficient coverage path planning in agricultural robots, outperforming traditional heuristics.

Findings

Achieves over 90% coverage while maintaining energy safety.

Outperforms heuristic algorithms by 13.4-19.5% in coverage.

Reduces energy constraint violations by 59.9-88.3%.

Abstract

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.