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.

GPS-based navigation in orchards can be unstable because trees may block the GPS signal or introduce multipath errors. Most research on robot navigation without GPS has focused on guidance inside orchard rows; end-of-row detection has not received enough attention. Additionally, navigation between rows relies on reference maps or artificial landmarks. In this work, a novel row-end detection method is presented, which detects drastic changes in the statistical distribution of the sensed point cloud as the robot gets closer to the row’s end. A row-entry method was also implemented that builds a local map that is used by a reactive path tracker. The system was evaluated in a 24-row block in a vineyard. Once the robot was closer than 7 m from the end of a row, the algorithm detected it with a 100% success rate and calculated the distance from it with a mean error of 0.54 m. The system was also evaluated in vineyard configurations with parallel and slanted vine rows in consecutive blocks. The system worked well in all configurations, except where the next block had rows aligned to the rows of the current block and the headland width was closer than 5 m.

Strawberries naturally grow in clusters, interwoven with leaves, stems, and other fruits, which frequently leads to occlusion. This inherent growth habit presents a significant challenge for robotic picking, as traditional percept-plan-control systems struggle to reach fruits amid the clutter. Effectively picking an occluded strawberry demands dexterous manipulation to carefully bypass or gently move the surrounding soft objects and precisely access the ideal picking point—located at the stem just above the calyx. To address this challenge, we introduce a strawberry-picking robotic system that learns from human demonstrations. Our system features a 4-DoF SCARA arm paired with a human teleoperation interface for efficient data collection. We propose the end pose assisted action chunking transformer (EPACT) to learn a fine-grained visuomotor policy. By incorporating end-pose supervision, our approach enables the robot to acquire dexterous manipulation skills such as pushing aside leaves or navigating tight clusters that are difficult to achieve with traditional pipelines. Experiments demonstrate that our method successfully transfers human-like dexterity to the robot, enabling clustered strawberry picking and outperforming imitation learning baselines, underscoring its potential for practical application in complex agricultural environments.

Autonomous remotely operated vehicles (ROVs) have emerged as a promising solution for fish pen inspections, replacing labor-intensive manual inspections and enhancing operational efficiency. However, deploying ROVs in these environments presents significant challenges, including unreliable localization, system uncertainties, and dynamic environmental disturbances. To address these issues, we propose a fully autonomous ROV system designed specifically for fish pen inspection in this paper. The system integrates a vision-aided approach for underwater localization and an effective path-planning algorithm to ensure safe navigation during inspections. To improve the system’s robustness against uncertainties and disturbances, we introduce a robust control scheme that combines two components: a nominal feedback controller that stabilizes the partially known nominal model of the ROV dynamics, and a sliding-mode compensator (SMC) that mitigates the effects of unknown dynamics and external disturbances. This robust control scheme, referred to as RC-SMC, minimizes the need for extensive parameter tuning while ensuring precise path-tracking underwater. Comprehensive experiments have been conducted in both laboratory and field environments to validate the efficacy and robustness of the proposed system. The results demonstrate that our ROV system can effectively perform autonomous inspections while maintaining improved stability and tracking precision compared to existing algorithms in the presence of various uncertainties and disturbances.