Autonomous Navigation of 4WIS4WID Agricultural Field Mobile Robot using Deep Reinforcement Learning

2024-12-25

AI Summary

Autonomous navigation of 4WIS4WID (four-wheel independent steering and four-wheel independent driving) field robots in unstructured agricultural environments—characterized by irregular crop rows, dense obstacles, confined spaces, and variable weather—remains a critical challenge for Agriculture 4.0. Method: This paper proposes a lateral-movement navigation framework based on deep reinforcement learning (DRL), pioneering the application of continuous-action-space DRL algorithms (SAC and TD3) to the 4WIS4WID configuration. We design a dual-parameterized four-wheel steering model enabling zero-radius turns and pure lateral translation, and integrate waypoint-guided control with a realistic crop-row simulation environment. Contribution/Results: The framework achieves high-precision single-row following and point-to-point cross-row navigation. Experimental evaluation demonstrates that SAC outperforms TD3 in navigation accuracy and robustness across multi-row scenarios, significantly enhancing operational autonomy and environmental adaptability in complex farmland settings.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Multiagent Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

In the futuristic agricultural fields compatible with Agriculture 4.0, robots are envisaged to navigate through crops to perform functions like pesticide spraying and fruit harvesting, which are complex tasks due to factors such as non-geometric internal obstacles, space constraints, and outdoor conditions. In this paper, we attempt to employ Deep Reinforcement Learning (DRL) to solve the problem of 4WIS4WID mobile robot navigation in a structured, automated agricultural field. This paper consists of three sections: parameterization of four-wheel steering configurations, crop row tracking using DRL, and autonomous navigation of 4WIS4WID mobile robot using DRL through multiple crop rows. We show how to parametrize various configurations of four-wheel steering to two variables. This includes symmetric four-wheel steering, zero-turn, and an additional steering configuration that allows the 4WIS4WID mobile robot to move laterally. Using DRL, we also followed an irregularly shaped crop row with symmetric four-wheel steering. In the multiple crop row simulation environment, with the help of waypoints, we effectively performed point-to-point navigation. Finally, a comparative analysis of various DRL algorithms that use continuous actions was carried out.

Problem

• Agricultural Robotics
• Autonomous Navigation
• Complex Farm Environments

Innovation

• Deep Reinforcement Learning
• Autonomous Navigation
• Agricultural Robotics