检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

100 篇 会议
45 篇 期刊文献

馆藏范围

145 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

70 篇 工学
- 36 篇 控制科学与工程
- 30 篇 计算机科学与技术...
- 27 篇 软件工程
- 13 篇 机械工程
- 10 篇 生物医学工程（可授...
- 8 篇 电子科学与技术（可...
- 7 篇 仪器科学与技术
- 7 篇 电气工程
- 6 篇 动力工程及工程热...
- 6 篇 生物工程
- 4 篇 光学工程
- 4 篇 建筑学
- 4 篇 土木工程
- 4 篇 化学工程与技术
- 4 篇 航空宇航科学与技...
- 4 篇 安全科学与工程
- 3 篇 力学（可授工学、理...
- 3 篇 材料科学与工程（可...
- 3 篇 信息与通信工程
- 2 篇 交通运输工程
35 篇 理学
- 17 篇 数学
- 11 篇 系统科学
- 10 篇 统计学（可授理学、...
- 8 篇 生物学
- 7 篇 物理学
- 4 篇 化学
10 篇 管理学
- 9 篇 管理科学与工程(可...
3 篇 医学
- 3 篇 临床医学
2 篇 经济学
- 2 篇 应用经济学
1 篇 法学
1 篇 军事学

主题

13 篇 service robots
9 篇 mobile robots
9 篇 robot sensing sy...
7 篇 automation
7 篇 process control
7 篇 trajectory
7 篇 robotics and aut...
6 篇 optimization
5 篇 three-dimensiona...
5 篇 planning
5 篇 neural networks
5 篇 cameras
5 篇 intelligent robo...
5 篇 sensors
4 篇 force
4 篇 robots
4 篇 helicopters
4 篇 hardware
4 篇 robot kinematics
4 篇 fault detection

机构

7 篇 process automati...
7 篇 state key labora...
6 篇 the institute of...
5 篇 karlsruhe univer...
4 篇 intelligent proc...
4 篇 tu darmstadt ins...
4 篇 tu darmstadt ins...
4 篇 state key labora...
4 篇 department for p...
3 篇 institute for an...
3 篇 the institute of...
3 篇 institute for an...
3 篇 university of ka...
3 篇 department for i...
3 篇 shenyang institu...
3 篇 department of co...
3 篇 university of he...
3 篇 school of mechan...
3 篇 institutes for r...
3 篇 state key labora...

作者

12 篇 torsten kröger
12 篇 bin xian
8 篇 jörg krüger
7 篇 björn hein
6 篇 gregor thiele
5 篇 hosam alagi
5 篇 xu zhang
5 篇 christoph lederm...
5 篇 yao zhang
5 篇 jonas c. kiemel
4 篇 schwung andreas
4 篇 zeng ming
4 篇 tamim asfour
4 篇 beck mark
4 篇 wörn h.
4 篇 xu xin-he
4 篇 axel vick
4 篇 muenchhof marco
4 篇 wang fei
4 篇 isermann rolf

语言

135 篇 英文
6 篇 中文
4 篇 其他

检索条件"机构=Process Automation and Robotics"

共 145 条记录，以下是31-40 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Sliding mode control based hybrid closed-loop design for stable walking of biped robot with heterogeneous legs

Sliding mode control based hybrid closed-loop design for sta...

引用

2011 IEEE International Conference on Mechatronics and automation, ICMA 2011

作者： Wang, Fei Wen, Shiguang Wu, Chengdong Cheng, Zhiyong State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 158100 China

ISBN: (纸本)9781424481149

Biped robot with heterogeneous legs (BRHL) is a new kind of robotic mode consist of artificial leg (AL) used to simulate healthy lower limb and bionic leg corresponding to lower limb prosthesis worn by amputee. The bionic leg recognizes and tracks the gait of AL in real-time to realize stable walking of the whole robotic system. To achieve smooth, effective and human-like gait for AL, s-curve interpolation was employed to fit the discrete data points generated by linear inverted pendulum model. A hybrid closed-loop system was constructed to improve the gait tracking accuracy and sliding mode control was designed for outer loop of the joints' servo system. Simulations and experiments were conducted under level walking condition. The results indicate the correctness and effectiveness of the proposed scheme. © 2011 IEEE.

关键词： Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Intelligent bionic leg motion estimation based on interjoint coordination using PCA and RBF neural networks

Intelligent bionic leg motion estimation based on interjoint...

引用

2012 9th IEEE International Conference on Mechatronics and automation, ICMA 2012

作者： Wang, Fei Qi, Yalu Wen, Shiguang Wu, Chengdong State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 158100 China

ISBN: (纸本)9781467312776

It has been a challenging endeavor for amputee to coordinate harmoniously with his/her artificial limb. In this paper, a novel scheme of real-time motion estimation for Intelligent bionic leg based on interjoint coordination is proposed. To measure the gait during walking, inertial sensors are mounted on the CoGs of bilateral thighs and shanks to acquire angular velocities of lower limbs of subjects. For the existence of linear correlation between bilateral kinematics in healthy symmetrical human gait, principle components analysis is employed to model the interjoint coordination and is used to estimate the knee joint angle of intelligent bionic leg from body motion of amputee. To improve the presicion of motion estimation further, RBF neural networks are used to optimally calculate the knee joint angle. Simulation and experimental results demonstrate the effectiveness and correctness of the proposed scheme. © 2012 IEEE.

关键词： Motion estimation

来源：评论

学校读者我要写书评

暂无评论

Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors

Learning Collision-free and Torque-limited Robot Trajectorie...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Jonas C. Kiemel Torsten Kröger Institute for Anthropomatics and Robotics - Intelligent Process Automation and Robotics (IAR-IPR) Karlsruhe Institute of Technology (KIT)

This paper presents an approach for learning online generation of collision-free and torque-limited robot trajectories. In order to generate future motions, a neural network is periodically invoked. Based on the current kinematic state of the robot and the network output, a trajectory for the current time interval can be calculated. The main idea of our paper is to execute the computed motion only if a collision-free and torque-limited way to continue the trajectory is known. In practice, the motion computed for the current time interval is extended by a braking trajectory and simulated using a physics engine. If the simulated trajectory complies with all safety constraints, the computed motion is carried out. Otherwise, the braking trajectory calculated in the previous time interval serves as an alternative safe behavior. Given a task-specific reward function, the neural network is trained using reinforcement learning. The design of the action space used for reinforcement learning ensures that all computed trajectories comply with kinematic joint limits. For our evaluation, simulated humanoid robots and industrial robots are trained to reach as many randomly placed target points as possible. We show that our method reliably prevents collisions with static obstacles and collisions between the robot arms, while generating motions that respect both torque limits and kinematic joint limits. Experiments with a real robot demonstrate that safe trajectories can be generated in real-time.

关键词： Torque Service robots Neural networks Humanoid robots Kinematics Reinforcement learning Trajectory

来源：评论

学校读者我要写书评

暂无评论

Learning Robot Trajectories subject to Kinematic Joint Constraints

Learning Robot Trajectories subject to Kinematic Joint Const...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Jonas C. Kiemel Torsten Kröger Institute for Anthropomatics and Robotics – Intelligent Process Automation and Robotics (IAR-IPR) Karlsruhe Institute of Technology (KIT)

We present an approach to learn fast and dynamic robot motions without exceeding limits on the position θ, velocity $\dot \theta $ , acceleration $\ddot \theta $ and jerk $\dddot \theta $ of each robot joint. Movements are generated by mapping the predictions of a neural network to safely executable joint accelerations. The neural network is invoked periodically and trained via reinforcement learning. Our main contribution is an analytical procedure for calculating safe joint accelerations, which considers the prediction frequency fN of the neural network. As a result, the frequency fN can be freely chosen and treated as a hyperparameter. We show that our approach is preferable to penalizing constraint violations as it provides explicit guarantees and does not distort the desired optimization target. In addition, the influence of the selected prediction frequency on the learning performance and on the computing effort is highlighted by various experiments.

关键词： Robot motion automation Conferences Neural networks Dynamics Reinforcement learning Kinematics

来源：评论

学校读者我要写书评

暂无评论

TrueRMA: Learning Fast and Smooth Robot Trajectories with Recursive Midpoint Adaptations in Cartesian Space

TrueRMA: Learning Fast and Smooth Robot Trajectories with Re...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Jonas C. Kiemel Pascal Meißner Torsten Kröger Institute for Anthropomatics and Robotics – Intelligent Process Automation and Robotics (IAR-IPR) Karlsruhe Institute of Technology (KIT)

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We present TrueRMA, a data-efficient, model-free method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate trajectory waypoints in Cartesian space by recursively predicting orthogonal adaptations relative to the midpoints of straight lines. We generate a differentiable path by adding circular blends around the waypoints, calculate the corresponding joint positions with an inverse kinematics solver and calculate a time-optimal parameterization considering velocity and acceleration limits. During training, the trajectory is executed in a physics simulator and costs are assigned according to a user-specified cost function which is not required to be differentiable. Given a starting point and an endpoint as input, a neural network is trained to predict midpoint adaptations that minimize the cost of the resulting trajectory via reinforcement learning. We successfully train a KUKA iiwa robot to keep a ball on a plate while moving between specified points and compare the performance of TrueRMA against two baselines. The results show that our method requires less training data to learn the task while generating shorter and faster trajectories.

关键词： Trajectory Robots Kinematics Task analysis Training Neural networks

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Domain Randomization Techniques for Transfer Learning

Evaluation of Domain Randomization Techniques for Transfer L...

引用

International Conference on Advanced robotics (ICAR)

作者： Silas Grün Simon Höninger Paul Maria Scheikl Björn Hein Torsten Kröger Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Karlsruhe Institute of Technology (KIT) Germany

To address the challenge of resource-intensive data collection from real robotic environments, many deep learning applications use synthetic data to train their networks. This creates new problems when transferring the obtained knowledge from the simulated to the real world domain. Various aspects of the simulation, which do not influence the learning objective, can be randomized to enhance generalization to new domains. In this paper, we analyze the effect of these domain randomization techniques. To get an insight into their benefits, we apply them while training a grasp success classifier based on state-of-the-art CNN for an industrial robot as a showcase. We generated a large synthetic data set containing 1.44M RGB images with 48 permutations of 6 different randomizations and a base scenario as training data. The resulting networks, each trained on a different subset of this data set, are evaluated on 3k real world images of the robot performing grasps. We observed the effectiveness of randomization of perspective, distractors, lighting and the grasped box. Notably, we show that pretrained networks benefit from these techniques in particular.

关键词：

来源：评论

学校读者我要写书评

暂无评论

TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space

TrueÆdapt: Learning Smooth Online Trajectory Adaptation wit...

引用

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jonas C. Kiemel Robin Weitemeyer Pascal Meißner Torsten Kröger Institute for Anthropomatics and Robotics - Intelligent Process Automation and Robotics (IAR-IPR) Karlsruhe Institute of Technology (KIT)

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

We present TrueÆdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the predicted accelerations, leading to continuously differentiable joint velocities and positions. Bounded jerks, accelerations and velocities are guaranteed by calculating the range of valid accelerations at each decision step and clipping the network's output accordingly. A deviation penalty during the training process causes the adapted trajectory to follow the original one. Smooth movements are encouraged by penalizing high accelerations and jerks. We evaluate our approach by training a simulated KUKA iiwa robot to balance a ball on a plate while moving and demonstrate that the balancing policy can be directly transferred to a real robot.

关键词： Training Adaptation models Robot sensing systems Real-time systems Trajectory Acceleration Task analysis

来源：评论

学校读者我要写书评

暂无评论

Jerk-limited Traversal of One-dimensional Paths and its Application to Multi-dimensional Path Tracking

Jerk-limited Traversal of One-dimensional Paths and its Appl...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Jonas C. Kiemel Torsten Kröger Institute for Anthropomatics and Robotics – Intelligent Process Automation and Robotics (IAR-IPR) Karlsruhe Institute of Technology (KIT)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we present an iterative method to quickly traverse multi-dimensional paths considering jerk constraints. As a first step, we analyze the traversal of each individual path dimension. We derive a range of feasible target accelerations for each intermediate waypoint of a one-dimensional path using a binary search algorithm. Computing a trajectory from waypoint to waypoint leads to the fastest progress on the path when selecting the highest feasible target acceleration. Similarly, it is possible to calculate a trajectory that leads to minimum progress along the path. This insight allows us to control the traversal of a one-dimensional path in such a way that a reference path length of a multi-dimensional path is approximately tracked over time. In order to improve the tracking accuracy, we propose an iterative scheme to adjust the temporal course of the selected reference path length. More precisely, the temporal region causing the largest position deviation is identified and updated at each iteration. In our evaluation, we thoroughly analyze the performance of our method using seven-dimensional reference paths with different path characteristics. We show that our method manages to quickly traverse the reference paths and compare the required traversing time and the resulting path accuracy with other state-of-the-art approaches.

关键词： Accuracy Target tracking Trajectory Iterative methods robotics and automation

来源：评论

学校读者我要写书评

暂无评论

The Komodo project: Thread-based event handling supported by a multithreaded Java microcontroller

The Komodo project: Thread-based event handling supported by...

引用

25th EUROMICRO Conference on Informatics: Theory and Practice for the New Millennium, EUROMICRO 1999

作者： Kreuzinger, J. Marston, R. Ungerer, Th. Brinkschulte, U. Krakowski, C. Dept. of Computer Design and Fault Tolerance University of Karlsruhe D-76128 Karlsruhe Germany Dept. for Process Control Automation and Robotics University of Karlsruhe D-76128 Karlsruhe Germany

ISBN: (纸本)0769503217

The Komodo project concerns the handling of multiple real-time events by Java threads that are supported by a multithreaded Java microcontroller. The architecture of the processor core and resulting implications are considered. The use of thread-based event handling is introduced and explained in combination with an Automatic Guided Vehicle (AGV) application. The advantages of thread-based event handling over a normal Interrupt Service Routine (ISR) strategy are demonstrated by the development of the AGV example. © 1999 IEEE.

关键词： Automatic guided vehicles

来源：评论

学校读者我要写书评

暂无评论

Robot Learning of Shifting Objects for Grasping in Cluttered Environments

Robot Learning of Shifting Objects for Grasping in Cluttered...

引用

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Lars Berscheid Pascal Meißner Torsten Kröger Intelligent Process Automation and Robotics Lab (IPR) Karlsruhe Institute of Technology (KIT)

Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pre-grasping manipulation like shifting or pushing an object becomes necessary. We developed an algorithm that can learn, in addition to grasping, to shift objects in such a way that their grasp probability increases. Our research contribution is threefold: First, we present an algorithm for learning the optimal pose of manipulation primitives like clamping or shifting. Second, we learn non-prehensible actions that explicitly increase the grasping probability. Making one skill (shifting) directly dependent on another (grasping) removes the need of sparse rewards, leading to more data-efficient learning. Third, we apply a real-world solution to the industrial task of bin picking, resulting in the ability to empty bins completely. The system is trained in a self-supervised manner with around 25 000 grasp and 2500 shift actions. Our robot is able to grasp and file objects with 274±3 picks per hour. Furthermore, we demonstrate the system's ability to generalize to novel objects.

关键词： Service robots Grasping Robot learning Clamps Task analysis Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共15页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：