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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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...
Lagrange Multipliers: Two Constraints01:28

Lagrange Multipliers: Two Constraints

The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.

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

Efficient image matching for UAV visual navigation via DALGlue.

Scientific reports·2025
Same author

Effect of scaling and root planing on TNF-α, IL-1β, and IL-10 levels in periodontitis patients with and without diabetes: a cross-sectional study.

BMC oral health·2025
Same author

Towards efficient glaucoma screening with modular convolution-involution cascade architecture.

PeerJ. Computer science·2025
Same author

Isorhamnetin ameliorates hyperuricemia by regulating uric acid metabolism and alleviates renal inflammation through the PI3K/AKT/NF-κB signaling pathway.

Food & function·2025
Same author

A Robust Approach to Early Glaucoma Identification from Retinal Fundus Images using Dirichlet-based Weighted Average Ensemble and Bayesian Optimization

Current medical imaging·2025
Same author

Enhanced Pneumonia Detection in Chest X-Rays Using Hybrid Convolutional and Vision Transformer Networks.

Current medical imaging·2025

相关实验视频

Updated: Jun 16, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K

基于深度强化学习在动态的多重障碍环境中的多UAV同时目标分配和路径规划.

Xiaoran Kong1, Yatong Zhou1, Zhe Li2

  • 1School of Electronic and Information Engineering, HeBei University of Technology, Tianjin, China.

Frontiers in neurorobotics
|February 6, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了多个无人机系统的深度强化学习算法,使得在动态环境中能够有效地分配目标和无碰撞路径规划.

关键词:
深度强化学习的学习.多个无人驾驶飞行器是多个无人驾驶飞行器.部分可观测的马尔科夫决策过程.路径规划路径规划路径规划目标分配的目标分配.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

542
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

152

相关实验视频

Last Updated: Jun 16, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

542
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

152

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
  • 控制系统 控制系统

背景情况:

  • 合作的多无人机系统需要有效的目标分配和路径规划.
  • 环境动态和部分可观测性对无人机协调构成重大挑战.

研究的目的:

  • 为应对多无人机在动态,部分可观测环境中的目标分配和路径规划的挑战.
  • 开发一种新的算法,集成目标分配和路径规划,以加强无人机协作.

主要方法:

  • 制定了多无人机目标分配和路径规划问题,作为一个部分可观测的马尔科夫决策过程 (POMDP).
  • 提出了一个深度强化学习 (DRL) 算法,在双延迟的深度决定性政策梯度 (TD3) 框架中包含一个目标分配网络.
  • 目标分配网络处理每个步骤的分配,而TD3优化路径规划并提供训练信号.

主要成果:

  • 拟议的DRL算法实现了无人机的最佳完整目标分配.
  • 证明了每个无人机在复杂的3D动态环境和多个障碍物中的无碰撞路径规划.
  • 与现有方法相比,该方法在完成目标和适应性方面表现出优异的表现.

结论:

  • 这种基于DRL的新算法有效地解决了多无人机系统的集成目标分配和路径规划问题.
  • 该方法在合作执行任务和环境适应性方面取得了显著的改进.
  • 这种方法为复杂的现实世界无人机应用提供了强大的解决方案.