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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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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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基于对象检测的物流环境中使用CNN的全向移动机器人的模型预测控制.

Stefan-Daniel Achirei1, Razvan Mocanu2, Alexandru-Tudor Popovici1

  • 1Department of Computer Engineering, "Gheorghe Asachi" Technical University of Iasi, 700050 Iasi, Romania.

Sensors (Basel, Switzerland)
|June 10, 2023
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概括

本研究介绍了一种定制训练的卷积神经网络 (CNN),用于在物流环境中检测物体,并为移动机器人进行了优化. 它还引入了一个预测控制器,用于使用CNN检测到的物体和LIDAR数据进行高效的导航.

关键词:
计算机视觉 计算机视觉卷积神经网络是一种卷积神经网络.深度感应 感应深度感应离散的时间模型模型.导航 导航 导航 导航 导航对象检测检测对象检测对象检测全方位的移动机器人预测控制算法 预测控制算法

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科学领域:

  • 机器人和人工智能 机器人和人工智能
  • 计算机视觉 计算机视觉
  • 控制系统工程 控制系统工程

背景情况:

  • 对象检测对于自主移动机器人来说至关重要,它们可以感知和与周围环境互动.
  • 卷积神经网络 (CNN) 具有先进的对象识别能力,特别是在物流等复杂环境中.
  • 整合环境感知与运动控制是机器人导航的一个关键研究领域.

研究的目的:

  • 在物流环境中开发和优化移动机器人平台的物体探测器.
  • 引入基于模型的预测控制器,用于使用CNN衍生物体地图和LIDAR数据指导全向机器人.
  • 提高仓库中移动机器人路径查找的安全性,最佳性和效率.

主要方法:

  • 一个定制的CNN模型使用内部获得的数据集在仓库环境中进行了对象检测的训练.
  • 该CNN模型被优化为在移动机器人的车载平台上部署.
  • 模拟了一个基于模型的预测控制器,使用对象检测输出和LIDAR数据导航一个全向机器人.

主要成果:

  • 通过在移动平台上使用定制训练的CNN实现了成功的对象检测.
  • 预测控制方法证明了基于检测到的物体的有效导航.
  • 综合系统显示出在物流环境中安全和高效的路线规划的潜力.

结论:

  • 定制训练和优化的CNN模型在物流场景中有效地执行移动机器人的对象检测.
  • 预测控制器利用CNN物体检测和LIDAR,使机器人能够高效地导航.
  • 这项研究有助于在复杂的操作环境中推进移动机器人的自主能力.