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  • 1.
    Kurtser, Polina
    et al.
    The Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.
    Ringdahl, Ola
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Rotstein, Nati
    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel .
    Berenstein, Ron
    The Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Rishon Lezion, Israel.
    Edan, Yael
    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel .
    In-field grape cluster size assessment for vine yield estimation using a mobile robot and a consumer level RGB-D camera2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 2, p. 2031-2038Article in journal (Refereed)
    Abstract [en]

    Current practice for vine yield estimation is based on RGB cameras and has limited performance. In this paper we present a method for outdoor vine yield estimation using a consumer grade RGB-D camera mounted on a mobile robotic platform. An algorithm for automatic grape cluster size estimation using depth information is evaluated both in controlled outdoor conditions and in commercial vineyard conditions. Ten video scans (3 camera viewpoints with 2 different backgrounds and 2 natural light conditions), acquired from a controlled outdoor experiment and a commercial vineyard setup, are used for analyses. The collected dataset (GRAPES3D) is released to the public. A total of 4542 regions of 49 grape clusters were manually labeled by a human annotator for comparison. Eight variations of the algorithm are assessed, both for manually labeled and auto-detected regions. The effect of viewpoint, presence of an artificial background, and the human annotator are analyzed using statistical tools. Results show 2.8-3.5 cm average error for all acquired data and reveal the potential of using lowcost commercial RGB-D cameras for improved robotic yield estimation.

  • 2.
    Wiberg, Viktor
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Wallin, Erik
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Nordjell, Tomas
    Swedish University of Agricultural Sciences.
    Servin, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Control of rough terrain vehicles using deep reinforcement learning2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 1, p. 390-397Article in journal (Refereed)
    Abstract [en]

    We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27 degrees, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.

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