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Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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相关实验视频

Updated: Jul 9, 2026

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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蛇优化器基于LSTM的无人起重机的UWB定位方法

Li Wang1,2,3, Guangxiao Fan1, Qiao Wang1,2,3

  • 1College of Electrical Engineering, Henan University of Technology, Zhengzhou City, Henan Province, China.

PloS one
|November 1, 2023
PubMed
概括

这项研究介绍了一种用于无人起重机系统的新型超宽带 (UWB) 定位方法,使用蛇优化器长短期内存 (SO-LSTM) 框架来精确追踪起重机的位置.

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科学领域:

  • 机器人和自动化机器人与自动化
  • 传感器技术 传感器技术
  • 机器学习 机器学习

背景情况:

  • 传统的起重机定位方法与负载摆动作斗争.
  • 准确的负载位置对于无人驾驶和智能起重机操作至关重要.
  • 现有的技术仅限于主要运输和托拉车定位.

研究的目的:

  • 为无人起重机系统开发一种新的超宽带 (UWB) 定位方法.
  • 为了实现对起重机的实时和精确定位.
  • 在动态环境中克服传统定位技术的局限性.

主要方法:

  • 实施多基站和多标签UWB系统.
  • 使用时间分割多重接入 (TDMA) 和双向测距 (TWR) 进行距离测量.
  • 用蛇优化器 (SO) 算法优化长短期内存 (LSTM) 网络超参数.

主要成果:

  • 该SO-LSTM方法实现了0.1125米的最大定位误差和0.0589米的RMSE.
  • 与最小平方 (LS) 相比,根平均平方误差 (RMSE) 的显著减少为63.39%.
  • 与LS相比,最大定位误差 (MPE) 显著减少了60.77%.

结论:

  • 拟议的SO-LSTM UWB定位方法为无人起重机系统提供了卓越的准确性和有效性.
  • 这种方法显著优于传统的LS和卡尔曼波器 (KF) 方法.
  • 可实现精确的实时负载定位,增强起重机自动化和安全性.