Navigating Narrow Spaces: A Comprehensive Framework for Agricultural Robots

Navigating within narrow spaces is a fundamental challenge in robotics, requiring precise localisation, localisation error recovery, dynamic path planning, and adaptive control for effective manoeuvring. This letter presents a modular and perception-driven navigation framework designed for constrained environments, focusing primarily on agricultural applications. The proposed method integrates a multi-step point cloud processing pipeline for robust local perception, including pole detection, boundary line estimation, and trajectory refinement to ensure safe and precise traversal by refining initial trajectories based on detected environmental constraints and dynamically adapting to kinematic limitations. Experimental validation in a real strawberry polytunnel demonstrates superior trajectory accuracy and control stability compared to state-of-the-art navigators, achieving an average lateral deviation of 0.08 $\pm$ 0.01 m. The adaptive trajectory tracking and regulated pure pursuit control of the framework contribute to consistent navigation, even under increased velocity constraints, outperforming the resilient timed elastic band (RTEB) and model predictive path integral (MPPI) methods. This modular and generalisable framework offers significant potential for advancing autonomous navigation in narrow-space applications.