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

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...

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

Multivariate recovery coupling in interdependent networks with cascading failure.

Chaos (Woodbury, N.Y.)·2023
Same author

Percolation transitions in interdependent networks with reinforced dependency links.

Chaos (Woodbury, N.Y.)·2022
Same author

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime.

Materials (Basel, Switzerland)·2022
Same author

A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution.

Materials (Basel, Switzerland)·2022
Same author

Effect of load-capacity heterogeneity on cascading overloads in networks.

Chaos (Woodbury, N.Y.)·2022
Same author

Fatigue Reliability Analysis Method of Reactor Structure Considering Cumulative Effect of Irradiation.

Materials (Basel, Switzerland)·2021

相关实验视频

Updated: Jun 23, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K

基于多代理深度增强学习的多无人机重新部署优化,面向群体性能恢复.

Qilong Wu1, Zitao Geng1, Yi Ren1

  • 1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了多个无人机系统 (多个UAV) 的新策略,以便在车辆丢失后快速重新部署. 多代理的深度强化学习方法提高了群体的表现,并确保了最佳的群体形成.

关键词:
无人机群重新部署 无人机群重新部署分布式重新配置策略.多代理的深度强化学习学习.无人驾驶飞行器 (UAV) 是一种无人驾驶飞行器.

更多相关视频

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.3K
Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

777

相关实验视频

Last Updated: Jun 23, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.3K
Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

777

科学领域:

  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术
  • 分布式系统 分布式系统

背景情况:

  • 分布式人工智能对于多无人机 (Multi-UAV) 操作至关重要.
  • 在车辆销毁后,多无人机的最佳重新部署带来了重大分布式重新配置 (DR) 挑战.

研究的目的:

  • 为多个无人机系统开发一种基于深度强化学习的新型多代理分布式重新配置策略 (DRS).
  • 优化多个无人机组的重新部署,以提高群体性能.

主要方法:

  • 设计了一个双层的DRS框架,利用QMIX网络进行群体级重新部署,并使用单个深度Q网络进行单组调整.
  • 用Python进行模拟,以评估拟议的战略.

主要成果:

  • 拟议的DRS有效地优化了多个无人机组的重新部署,从而提高了群体性能.
  • 该战略通过案例研究验证,在大规模场景中展示了高质量的绩效.

结论:

  • 开发的基于深度强化学习的多代理DRS为多无人机系统的动态重新配置提供了有效的解决方案.
  • 这种方法在复杂的操作环境中提高了多无人机群的弹性和性能.