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首页> 外文期刊>Journal of Field Robotics >Vision-based Obstacle Detection and Navigation for an Agricultural Robot
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Vision-based Obstacle Detection and Navigation for an Agricultural Robot

机译：农业机器人的基于视觉的障碍物检测和导航

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摘要

This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows.
机译：本文介绍了一种基于视觉的障碍物检测和导航系统，该系统将用作机器人解决方案的一部分，以实现大面积农业的可持续集约化。为了具有成本效益，机器人解决方案必须与当前的人为驱动的农机具竞争。高端定位和障碍物检测传感器的成本很高。我们的系统演示了廉价的全球定位系统和惯性导航系统（带用于定位的视觉系统）和单个立体视觉系统（用于障碍物检测）的组合。本文介绍了机器人的设计，包括系统三个关键部分的详细说明：基于新颖性的障碍物检测，视觉辅助制导以及生成无碰撞运动学可行路径的导航系统。在白天和黑夜的数周现场试验中，该机器人都进行了广泛的测试。本文的结果与一个特定的3小时夜间实验有关，在该实验中，机器人执行了覆盖任务并避免了障碍。白天的其他结果表明，通过目视跟踪作物行，机器人可以在5分钟的GPS中断期间继续运行。

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  • 来源
    《Journal of Field Robotics》 |2016年第8期|1107-1130|共24页
  • 作者

    David Ball; Ben Upcroft; Gordon Wyeth; Peter Corke; Andrew English; Patrick Ross; Tim Patten; Robert Fitch; Salah Sukkarieh; Andrew Bate;

  • 作者单位

    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4001, Australia and ARC Centre of Excellence for Robotic Vision, Australia;

    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4001, Australia and ARC Centre of Excellence for Robotic Vision, Australia;

    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4001, Australia and ARC Centre of Excellence for Robotic Vision, Australia;

    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4001, Australia and ARC Centre of Excellence for Robotic Vision, Australia;

    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4001, Australia;

    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4001, Australia;

    Australian Centre for Field Robotics, The University of Sydney, Sydney NSW 2006, Australia;

    Australian Centre for Field Robotics, The University of Sydney, Sydney NSW 2006, Australia;

    Australian Centre for Field Robotics, The University of Sydney, Sydney NSW 2006, Australia;

    SwarmFarm Robotics, Queensland, Australia;

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  • 正文语种 eng
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