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相关概念视频

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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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Robotic Sensing and Stimuli Provision for Guided Plant Growth
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AgriPath:一个强大的多目标路径规划框架,用于农业机器人在动态的田野环境中.

Chenghan Yang1, Dingkun Zheng1, Siming Chen2

  • 1Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan.

Frontiers in plant science
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概括

本研究介绍了AgriPath,这是农业机器人路径规划的新框架. 通过使用先进的AI算法,AgriPath提高了复杂领域的导航效率和障碍回避.

关键词:
多目标优化多目标优化路径规划路径规划路径规划精准农业 精准农业 精准农业机器人技术 机器人工程 机器人工程鱼优化算法 鱼优化算法

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 精准农业 精准农业 精准农业

背景情况:

  • 农业机器人需要有效的路径规划,以进行复杂的野外导航.
  • 传统的算法与动态障碍,密集的植被和非结构化的地形作斗争.
  • 现有的方法缺乏在精准农业环境中的效率和适应性.

研究的目的:

  • 推出AgriPath,一个强大的农业机器人的多目标路径规划框架.
  • 加强在具有挑战性的农业环境中寻找路径,提高融合效率和避免障碍.
  • 为准确农业中自主导航提供更具适应性的解决方案.

主要方法:

  • AgriPath集成了改进的卷积神经网络 (CNN) 与因果卷积和自我注意力,用于轨迹预测.
  • 一个改进的A*算法利用动态启发函数 (NDVI) 和卡尔曼过来实现全局路径适应性.
  • 一个改进的鱼优化算法 (IWOA) 平衡了路径长度,流性和规划时间,并补充了道格拉斯-皮克克和B-spline平滑.

主要成果:

  • 在实验中,AgriPath表现出比SBREA*,殖民地A*,果园A*和贪A*更高的性能.
  • 该框架实现了更好的路径长度,流性,规划时间和动态避障成功率.
  • 结果表明,农业机器人导航的优越多目标优化平衡.

结论:

  • AgriPath显著提高了农业机器人路径规划的效率和稳定性.
  • 该框架为精密农业中的自主导航提供了更具适应性的解决方案.
  • 这项研究为农业机器人中的路径规划提供了新的理论和实践方向.