咨询与建议

限定检索结果

文献类型

  • 212 篇 期刊文献
  • 9 篇 会议

馆藏范围

  • 221 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 221 篇 工学
    • 215 篇 控制科学与工程
    • 209 篇 电气工程
    • 13 篇 计算机科学与技术...
    • 1 篇 机械工程
    • 1 篇 信息与通信工程
    • 1 篇 软件工程
  • 6 篇 管理学
    • 6 篇 管理科学与工程(可...
  • 1 篇 医学
    • 1 篇 基础医学(可授医学...

主题

  • 221 篇 deep learning in...
  • 23 篇 visual learning
  • 20 篇 perception for g...
  • 19 篇 task analysis
  • 18 篇 motion and path ...
  • 18 篇 localization
  • 18 篇 computer vision ...
  • 16 篇 visual-based nav...
  • 16 篇 robots
  • 15 篇 object detection
  • 14 篇 segmentation and...
  • 14 篇 semantic scene u...
  • 12 篇 computer vision ...
  • 12 篇 training
  • 11 篇 learning from de...
  • 11 篇 force and tactil...
  • 11 篇 learning and ada...
  • 11 篇 slam
  • 10 篇 grasping
  • 9 篇 robot sensing sy...

机构

  • 6 篇 hong kong univ s...
  • 5 篇 univ michigan de...
  • 5 篇 georgia inst tec...
  • 5 篇 univ perugia dep...
  • 4 篇 imperial coll lo...
  • 4 篇 stanford univ st...
  • 4 篇 natl univ singap...
  • 3 篇 swiss fed inst t...
  • 3 篇 univ hong kong d...
  • 3 篇 carnegie mellon ...
  • 3 篇 city univ hong k...
  • 3 篇 kth royal inst t...
  • 3 篇 univ michigan de...
  • 2 篇 seoul natl univ ...
  • 2 篇 univ adelaide sc...
  • 2 篇 georgia inst tec...
  • 2 篇 mit comp sci & a...
  • 2 篇 karlsruhe inst t...
  • 2 篇 swiss fed inst t...
  • 2 篇 carnegie mellon ...

作者

  • 8 篇 liu ming
  • 5 篇 costante gabriel...
  • 5 篇 yang guang-zhong
  • 5 篇 bohg jeannette
  • 5 篇 calandra roberto
  • 5 篇 johnson-roberson...
  • 3 篇 kumar vijay
  • 3 篇 chen steven w.
  • 3 篇 sartoretti guill...
  • 3 篇 tai lei
  • 3 篇 rus daniela
  • 3 篇 zhou xiao-yun
  • 3 篇 pan jia
  • 3 篇 vasudevan ram
  • 3 篇 davison andrew j...
  • 3 篇 choi changhyun
  • 3 篇 folkesson john
  • 3 篇 kelly jonathan
  • 3 篇 kawai hisashi
  • 3 篇 magassouba aly

语言

  • 221 篇 英文
检索条件"主题词=deep learning in robotics and automation"
221 条 记 录,以下是111-120 订阅
排序:
RANUS: RGB and NIR Urban Scene Dataset for deep Scene Parsing
收藏 引用
IEEE robotics AND automation LETTERS 2018年 第3期3卷 1808-1815页
作者: Choe, Gyeongmin Kim, Seong-Heum Im, Sunghoon Lee, Joon-Young Narasimhan, Srinivasa G. Kweon, In So Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Adobe Res San Jose CA 95110 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA
In this letter, we present a data-driven method for scene parsing of road scenes to utilize single-channel near-infrared (NIR) images. To overcome the lack of data problem in non-RGB spectrum, we define a new color sp... 详细信息
来源: 评论
Shear, Torsion and Pressure Tactile Sensor via Plastic Optofiber Guided Imaging
收藏 引用
IEEE robotics AND automation LETTERS 2020年 第2期5卷 2618-2625页
作者: Baimukashev, Daulet Kappassov, Zhanat Varol, Huseyin Atakan Nazarbayev Univ Inst Smart Syst & Artificial Intelligence Nur Sultan 010000 Kazakhstan Nazarbayev Univ Dept Robot & Mechatron Nur Sultan 010000 Kazakhstan
Object manipulation performed by robots refers to the art of controlling the shape and location of an object through force constraints with robot end-effectors, both robot hands, and grippers. The success of task exec... 详细信息
来源: 评论
learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems
收藏 引用
IEEE robotics AND automation LETTERS 2018年 第4期3卷 4383-4390页
作者: James, Jasmin Ford, Jason J. Molloy, Timothy L. Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4000 Australia
The commercial use of unmanned aerial vehicles (UAVs) would be enhanced by an ability to sense and avoid potential mid-air collision threats. In this letter, we propose a new approach to aircraft detection for long-ra... 详细信息
来源: 评论
DRL-VO: learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles
收藏 引用
IEEE TRANSACTIONS ON robotics 2023年 第4期39卷 2700-2719页
作者: Xie, Zhanteng Dames, Philip Temple Univ Dept Mech Engn Philadelphia PA 19122 USA
This article proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and de... 详细信息
来源: 评论
Constrained Motion Planning Networks X
收藏 引用
IEEE TRANSACTIONS ON robotics 2022年 第2期38卷 868-886页
作者: Qureshi, Ahmed Hussain Dong, Jiangeng Baig, Asfiya Yip, Michael C. Univ Calif San Diego La Jolla CA 92093 USA Univ Calif San Diego Elect & Comp Engn La Jolla CA 92093 USA
Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find a collision-free path on the constraint manifolds between a given start and goal configuration... 详细信息
来源: 评论
Aerial Single-View Depth Completion With Image-Guided Uncertainty Estimation
收藏 引用
IEEE robotics AND automation LETTERS 2020年 第2期5卷 1055-1062页
作者: Teixeira, Lucas Oswald, Martin R. Pollefeys, Marc Chli, Margarita Swiss Fed Inst Technol Vis Robot Lab CH-8050 Zurich Switzerland Swiss Fed Inst Technol Comp Vis & Geometry Grp CH-8092 Zurich Switzerland Microsoft Res CH-8001 Zurich Switzerland
On the pursuit of autonomous flying robots, the scientific community has been developing onboard real-time algorithms for localisation, mapping and planning. Despite recent progress, the available solutions still lack... 详细信息
来源: 评论
Self-Supervised Drivable Area and Road Anomaly Segmentation Using RGB-D Data For Robotic Wheelchairs
收藏 引用
IEEE robotics AND automation LETTERS 2019年 第4期4卷 4386-4393页
作者: Wang, Hengli Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Clear Water Bay Hong Kong Peoples R China
The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentation using deep learning techniques has... 详细信息
来源: 评论
deepIG: Multi-Robot Information Gathering With deep Reinforcement learning
收藏 引用
IEEE robotics AND automation LETTERS 2019年 第3期4卷 3059-3066页
作者: Viseras, Alberto Garcia, Ricardo German Aerosp Ctr DLR Inst Commun & Nav D-82234 Oberplaffenholen Germany Univ Politecn Madrid ETSI Telecomunicac E-28040 Madrid Spain
State-of-the-art multi-robot information gathering (MR-IG) algorithms often rely on a model that describes the structure of the information of interest to drive the robots motion. This causes MR-IG algorithms to fail ... 详细信息
来源: 评论
Motion Switching With Sensory and Instruction Signals by Designing Dynamical Systems Using deep Neural Network
收藏 引用
IEEE robotics AND automation LETTERS 2018年 第4期3卷 3481-3488页
作者: Suzuki, Kanata Mori, Hiroki Ogata, Tetsuya Fujitsu Labs Ltd Artificial Intelligence Labs Kawasaki Kanagawa 2118588 Japan Waseda Univ Sch Fundamental Sci & Engn Dept Intermedia Art & Sci Tokyo 5650871 Japan Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tokyo 1350064 Japan
To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of task motions suited to each situation wo... 详细信息
来源: 评论
High-Speed Autonomous Drifting With deep Reinforcement learning
收藏 引用
IEEE robotics AND automation LETTERS 2020年 第2期5卷 1247-1254页
作者: Cai, Peide Mei, Xiaodong Tai, Lei Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Alibaba Grp AI Lab Hangzhou 311000 Peoples R China
Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled pre... 详细信息
来源: 评论