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

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.

I. Introduction

In Modern farming, autonomous agricultural vehicles (AAVs) have gained significant popularity [1]. These vehicles navigate through crop rows and perform headland maneuvers to transition between rows, allowing them to cover the entire field [2]. Effective trajectory planning is crucial to ensure safe and efficient farm operations. In arable farming, the absence of dense obstacles simplifies trajectory planning, while the flexible cultivation layout eases the replanting of crops. These factors considerably facilitate automation and reduce operational challenges for AAVs.