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

2025-01-18

AI Summary

In orchards—characterized by narrow rows, irregular boundaries, and dense obstacles—achieving both safety and efficiency in headland turning trajectory planning remains challenging. Method: This paper proposes a hierarchical trajectory planning framework: an improved RRT* algorithm for efficient spatial exploration in the front end, and a Sequential Quadratic Programming (SQP) solver in the back end that jointly enforces agricultural vehicle-implement coupled kinematic constraints and nonlinear collision-avoidance constraints. Contribution/Results: To the best of our knowledge, this is the first work to generate dynamically consistent, real-time verifiable, and safety-guaranteed trajectories for autonomous agricultural vehicles. Experiments in real orchard environments demonstrate a 37% improvement in turning success rate and an average planning time of less than 85 ms—significantly outperforming state-of-the-art methods. The framework has been successfully deployed across multiple trailer-coupled agricultural platforms.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Autonomous Agricultural Vehicles
• Efficient and Safe Turning Paths
• Complex Orchard Environments

Innovation

• Novel Path Planning Algorithm
• Autonomous Agricultural Vehicles
• Orchard Navigation