Publications

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

Published in IEEE Robotics and Automation Letter, 2025

Headland maneuvering is a crucial part of the field operations performed by autonomous agricultural vehicles (AAVs). While motion planning for headland turning in open fields has been extensively studied and integrated into commercial autoguidance systems, the existing methods primarily address scenarios with ample headland space and thus may not work in more constrained headland geometries.

Optimization-based motion planning for autonomous agricultural vehicles turning in constrained headlands

Published in Journal of Field Robotics (JFR), 2024

Headland maneuvering is a crucial part of the field operations performed by autonomous agricultural vehicles (AAVs). While motion planning for headland turning in open fields has been extensively studied and integrated into commercial autoguidance systems, the existing methods primarily address scenarios with ample headland space and thus may not work in more constrained headland geometries.

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GNSS-Free End-of-Row Detection and Headland Maneuvering for Orchard Navigation Using a Depth Camera

Published in Machines, 2023

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.

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Depth camera based row end detection and headland manuvering in orchard navigation without GNSS

Published in 2022 30th Mediterranean Conference on Control and Automation (MED), 2022

A robust navigation system is a prerequisite for a mobile robotic platform to carry out precision agriculture tasks in a modern orchard. In contrast to open fields, navigation based solely on the Global Navigation Satellite System (GNSS) is not stable in many orchards, where tree canopies may block the GNSS signal or introduce multipath. Many works have been done to localize the robot while traversing the inside a row, but navigating the robot to the next row on headland still relies on a reference map or artificial landmarks. In this work, we developed a row end detection method by exploiting drastic changes in the statistical distribution of points sensed by a depth camera compared to the points inside the row. Also, a robust way of row entry method is implemented by building a local environment map and reactive path tracker. The whole navigation system is tested and evaluated on a mobile robot in a vineyard. The experiment results show the robot can detect the tree row-end accurately and maneuver a U-turn to the next row safely.

A strawberry harvest‐aiding system with crop‐transport collaborative robots: Design, development, and field evaluation

Published in Jounal of Field Robotics (JFR), 2021

This work presents the implementation and integration of the co-robotic harvest-aid system and its deployment during commercial strawberry harvesting. The evaluation experiments demonstrated that the proof-of-concept system was fully functional. The co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3.

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Deterministic predictive dynamic scheduling for crop-transport robots acting as harvesting-aids

Published in Computers and Electronics in Agriculture, 2020

In this paper,dynamic predictive scheduling was modeled for teams of robots carrying trays during manual harvesting. The times and locations of the tray-transport requestswere assumed to be known exactly (deterministic predictions). Near-optimal scheduling was implemented to provide efficiency upper-boundsfor any predictive scheduling algorithms that incorporate uncertainty in the predictive requests. Robot-aided harvesting was simulated using manual-harvest data collected from a commercial picking crew. Scheduling performance was studied as a function of the number of robots, for a given crew size, and as a function of robot speed. Additionally, the effect of the earliness of the availability of the predictionson performance was studied.

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Collaboration of Human Pickers and Crop-transporting Robots during Harvesting - PartII: Simulator Evaluation and Robot-Scheduling Case-study

Published in Computers and Electronics in Agriculture, 2020

In Part I of this work, a modeling framework, and a stochastic simulator were presented for all-manual and robot-aided harvesting. This paper reports Part II of our work, which utilized data gathered in two strawberry fields during harvesting, to estimate the stochastic parameters involved in modeling pickers, and evaluate the prediction accuracy of the simulator for all-manual picking. Then, as a case study, non-productive time and harvest efficiency were estimated for robot-aided harvesting, for various picker-robot ratios and three priority-based reactive dispatching strategies for the robots.

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Collaboration of Human Pickers and Crop-transporting Robots during Harvesting - Part I: Model and Simulator Development

Published in Computers and Electronics in Agriculture, 2020

Some specialty crops, such as strawberries and table grapes, are harvested by large crews of pickers who spend significant amounts of time carrying empty and full (with the harvested crop) trays. A step toward increasing harvest automation for such crops is to deploy harvest-aid robots that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers’ non-productive walking times.

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