Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

47
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
47
Sampling Plans01:23

Sampling Plans

180
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
180

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications.

Sensors (Basel, Switzerland)·2024
查看所有相关文章

相关实验视频

Updated: Jun 25, 2025

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

6.8K

基于采样的深度学习增强路径规划用于LTL任务规范.

Changmin Baek1, Kyunghoon Cho1

  • 1Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Republic of Korea.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括

本研究介绍了一种新的路径规划算法,用于满足任务要求的低成本轨迹,使用线性时间逻辑 (LTL). 该方法使用一种新的离散抽象和深度学习,以实现高效,优越的轨迹生成.

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
  • 控制理论 控制理论

背景情况:

  • 路径规划对于自主系统至关重要.
  • 现有的方法难以应对复杂的环境和任务规范.
  • 线性时间逻辑 (LTL) 提供了一种正式的方式来定义任务要求.

研究的目的:

  • 为低成本轨迹引入一种新的路径规划算法.
  • 履行LTL.中规定的任务要求.
  • 完善复杂环境的采样方法.

主要方法:

  • 开发了一个多层框架,用于在没有网状分解的情况下进行离散抽象.
  • 集成深度学习以建模最佳轨迹分布.
  • 使用离散抽象进行顶点和目标选择的引导采样过程.

主要成果:

  • 成功生成了符合LTL任务标准的低成本轨迹.
  • 在复杂和高维环境中证明有效性.
  • 与模拟中现有的路径规划方法相比,实现了更高的性能.

结论:

关键词:
基于深度学习的控制合成.正式的方法 正式的方法基于任务的路径规划.基于采样的路径规划.

更多相关视频

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.6K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

相关实验视频

Last Updated: Jun 25, 2025

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

6.8K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.6K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
  • 拟议的算法为基于LTL的路径规划提供了高效和有效的解决方案.
  • 离散抽象和深度学习集成是其成功的关键.
  • 这种方法推进了自主导航和任务规划的最新技术.