Optimizing Path Planning using Deep Reinforcement Learning for UGVs in Precision Agriculture

TL;DR

This paper presents a novel application of deep reinforcement learning, specifically continuous action space algorithms like TD3, to optimize path planning for UGVs in dynamic agricultural environments, outperforming traditional methods.

Contribution

It introduces the use of advanced continuous DRL algorithms for UGV path planning in agriculture, demonstrating improved adaptability and success rates over traditional grid-based methods.

Findings

TD3 achieves 95% success rate in dynamic scenarios

Continuous DRL outperforms traditional grid-based algorithms

Pretrained models effectively handle moving obstacles

Abstract

This study focuses on optimizing path planning for unmanned ground vehicles (UGVs) in precision agriculture using deep reinforcement learning (DRL) techniques in continuous action spaces. The research begins with a review of traditional grid-based methods, such as A* and Dijkstra's algorithms, and discusses their limitations in dynamic agricultural environments, highlighting the need for adaptive learning strategies. The study then explores DRL approaches, including Deep Q-Networks (DQN), which demonstrate improved adaptability and performance in two-dimensional simulations. Enhancements such as Double Q-Networks and Dueling Networks are evaluated to further improve decision-making. Building on these results, the focus shifts to continuous action space models, specifically Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3), which are tested in increasingly complex environments. Experiments conducted in a three-dimensional environment using ROS and Gazebo demonstrate the effectiveness of continuous DRL algorithms in navigating dynamic agricultural scenarios. Notably, the pretrained TD3 agent achieves a 95 percent success rate in dynamic environments, demonstrating the robustness of the proposed approach in handling moving obstacles while ensuring safety for both crops and the robot.