• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一种新的灾难信息感知模式:使用具有空气散布模式的多机器人系统。

A New Disaster Information Sensing Mode: Using Multi-Robot System with Air Dispersal Mode.

机构信息

Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China.

出版信息

Sensors (Basel). 2018 Oct 22;18(10):3589. doi: 10.3390/s18103589.

DOI:10.3390/s18103589
PMID:30360444
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6210509/
Abstract

This paper presents a novel sensing mode for using mobile robots to collect disaster ground information when the ground traffic from the rescue center to disaster site is disrupted. Traditional sensing modes which use aerial robots or ground robots independently either have limited ability to access disaster site or are only able to provide a bird's eye view of the disaster site. To illustrate the proposed sensing mode, the authors have developed a Multi-robot System with Air Dispersal Mode (MSADM) by combining the unimpeded path of aerial robots with the detailed view of ground robots. In the MSADM, an airplane carries some minimal reconnaissance ground robots to overcome the paralyzed traffic problem and deploys them on the ground to collect detailed scene information using parachutes and separation device modules. In addition, the airplane cruises in the sky and relays the control and reported information between the ground robots and the human operator. This means that the proposed sensing mode is able to provide more reliable communication performance when there are obstacles between the human operators and the ground robots. Additionally, the proposed sensing mode can easily make use of different kinds of ground robots, as long as they have a compatible interface with the separation device. Finally, an experimental demonstration of the MSADM is presented to show the effectiveness of the proposed sensing mode.

摘要

本文提出了一种新的传感模式,用于在从救援中心到灾难现场的地面交通中断时,使用移动机器人来收集灾难现场的信息。传统的传感模式,无论是使用空中机器人还是地面机器人独立进行,要么进入灾难现场的能力有限,要么只能提供灾难现场的鸟瞰图。为了说明所提出的传感模式,作者通过将空中机器人的无障碍路径与地面机器人的详细视图相结合,开发了一种具有空中散布模式的多机器人系统(MSADM)。在 MSADM 中,飞机携带一些最小的侦察地面机器人来克服瘫痪的交通问题,并使用降落伞和分离装置模块将它们部署在地面上以收集详细的场景信息。此外,飞机在天空中巡航,并在地面机器人和操作人员之间中继控制和报告信息。这意味着,在所提出的传感模式中,当操作人员和地面机器人之间存在障碍物时,能够提供更可靠的通信性能。此外,所提出的传感模式可以轻松利用不同种类的地面机器人,只要它们与分离装置具有兼容的接口即可。最后,展示了 MSADM 的实验演示,以展示所提出的传感模式的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/a8d690ad2cde/sensors-18-03589-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/f63b4b151a55/sensors-18-03589-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/26fb4c1f6a3b/sensors-18-03589-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/a41b4baeaaac/sensors-18-03589-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/802bb4a5c761/sensors-18-03589-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/603bd99e6af3/sensors-18-03589-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/1720916d2e92/sensors-18-03589-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/45ac930134eb/sensors-18-03589-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/67472e8dff3f/sensors-18-03589-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/d0fb9fc1546d/sensors-18-03589-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/b5b9c028db8f/sensors-18-03589-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/52d303488bf7/sensors-18-03589-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/733123806efb/sensors-18-03589-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/0b281ad2d460/sensors-18-03589-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/ac737474dbe1/sensors-18-03589-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/daee594a55db/sensors-18-03589-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/a8d690ad2cde/sensors-18-03589-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/f63b4b151a55/sensors-18-03589-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/26fb4c1f6a3b/sensors-18-03589-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/a41b4baeaaac/sensors-18-03589-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/802bb4a5c761/sensors-18-03589-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/603bd99e6af3/sensors-18-03589-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/1720916d2e92/sensors-18-03589-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/45ac930134eb/sensors-18-03589-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/67472e8dff3f/sensors-18-03589-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/d0fb9fc1546d/sensors-18-03589-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/b5b9c028db8f/sensors-18-03589-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/52d303488bf7/sensors-18-03589-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/733123806efb/sensors-18-03589-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/0b281ad2d460/sensors-18-03589-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/ac737474dbe1/sensors-18-03589-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/daee594a55db/sensors-18-03589-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b909/6210509/a8d690ad2cde/sensors-18-03589-g016.jpg

相似文献

1
A New Disaster Information Sensing Mode: Using Multi-Robot System with Air Dispersal Mode.一种新的灾难信息感知模式:使用具有空气散布模式的多机器人系统。
Sensors (Basel). 2018 Oct 22;18(10):3589. doi: 10.3390/s18103589.
2
A 2.5D Map-Based Mobile Robot Localization via Cooperation of Aerial and Ground Robots.一种通过空中和地面机器人协作实现的基于2.5D地图的移动机器人定位
Sensors (Basel). 2017 Nov 25;17(12):2730. doi: 10.3390/s17122730.
3
A Search-and-Rescue Robot System for Remotely Sensing the Underground Coal Mine Environment.一种用于远程感知煤矿井下环境的搜索救援机器人系统。
Sensors (Basel). 2017 Oct 23;17(10):2426. doi: 10.3390/s17102426.
4
ITC: Infused Tangential Curves for Smooth 2D and 3D Navigation of Mobile Robots .ITC:用于移动机器人平滑 2D 和 3D 导航的注入切向曲线。
Sensors (Basel). 2019 Oct 10;19(20):4384. doi: 10.3390/s19204384.
5
AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training.AiRobSim:模拟用于城市搜索和救援行动与训练的多传感器空中机器人。
Sensors (Basel). 2020 Sep 13;20(18):5223. doi: 10.3390/s20185223.
6
A Transformable Sheet Type Robot That Can Be Thrown from the Air.一种可从空中投掷的可变形片状机器人。
Biomimetics (Basel). 2022 Aug 16;7(3):114. doi: 10.3390/biomimetics7030114.
7
Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing.具有远程障碍物知识共享的多机器人系统中的共生导航
Sensors (Basel). 2017 Jul 5;17(7):1581. doi: 10.3390/s17071581.
8
UAV-guided navigation for ground robot tele-operation in a military reconnaissance environment.无人机引导地面机器人在军事侦察环境中的远程操作导航。
Ergonomics. 2010 Aug;53(8):940-50. doi: 10.1080/00140139.2010.500404.
9
Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning.利用移动机器人、固定传感器和机器学习进行多领域气流建模和通风特性描述。
Sensors (Basel). 2019 Mar 5;19(5):1119. doi: 10.3390/s19051119.
10
HazBot: Development of a telemanipulator robot with haptics for emergency response.哈兹博特:一款用于应急响应的带有触觉的远程操作机器人的研发。
Am J Disaster Med. 2008 Mar-Apr;3(2):87-97.

引用本文的文献

1
A Method of Merging Maps for MUAVs Based on an Improved Genetic Algorithm.一种基于改进遗传算法的多无人机地图融合方法。
Sensors (Basel). 2023 Jan 1;23(1):447. doi: 10.3390/s23010447.
2
State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios.用于不同场景的最先进的移动辐射探测系统。
Sensors (Basel). 2021 Feb 4;21(4):1051. doi: 10.3390/s21041051.
3
A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control.基于深度强化学习和双同步控制的面向多任务的机械臂运动规划方案。

本文引用的文献

1
A Search-and-Rescue Robot System for Remotely Sensing the Underground Coal Mine Environment.一种用于远程感知煤矿井下环境的搜索救援机器人系统。
Sensors (Basel). 2017 Oct 23;17(10):2426. doi: 10.3390/s17102426.
2
A Camera-Based Target Detection and Positioning UAV System for Search and Rescue (SAR) Purposes.一种用于搜索和救援(SAR)目的的基于摄像头的目标检测与定位无人机系统。
Sensors (Basel). 2016 Oct 25;16(11):1778. doi: 10.3390/s16111778.
3
Development and Testing of a Two-UAV Communication Relay System.一种双无人机通信中继系统的开发与测试
Sensors (Basel). 2020 Jun 21;20(12):3515. doi: 10.3390/s20123515.
Sensors (Basel). 2016 Oct 13;16(10):1696. doi: 10.3390/s16101696.