Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments

TL;DR

This paper introduces a novel trajectory planning method for autonomous agricultural vehicles that improves safety and efficiency during headland turning in cluttered orchard environments, addressing challenges posed by limited space and obstacles.

Contribution

It presents an innovative trajectory planner combining efficient front-end algorithms and high-performance back-end optimization tailored for complex orchard settings.

Findings

Outperforms existing methods in standard orchard fields

Enhances safety and efficiency in headland turning maneuvers

Applicable to vehicles with various implements

Abstract

Autonomous agricultural vehicles (AAVs), including field robots and autonomous tractors, are becoming essential in modern farming by improving efficiency and reducing labor costs. A critical task in AAV operations is headland turning between crop rows. This task is challenging in orchards with limited headland space, irregular boundaries, operational constraints, and static obstacles. While traditional trajectory planning methods work well in arable farming, they often fail in cluttered orchard environments. This letter presents a novel trajectory planner that enhances the safety and efficiency of AAV headland maneuvers, leveraging advancements in autonomous driving. Our approach includes an efficient front-end algorithm and a high-performance back-end optimization. Applied to vehicles with various implements, it outperforms state-of-the-art methods in both standard and challenging orchard fields. This work bridges agricultural and autonomous driving technologies, facilitating a broader adoption of AAVs in complex orchards.