2019 4th International Conference on Robotics and Automation Engineering (ICRAE)最新文献_第2页

Comparative Assessment of Reinforcement Learning Algorithms in the Taskof Robotic Manipulation of Deformable Linear Objects 强化学习算法在可变形线性物体机器人操作任务中的比较评估

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043790

Michał Bednarek, K. Walas

引用次数: 2

A Novel Loop Control Method of Full Attitude Inertial Stabilization Platform 一种新的全姿态惯性稳定平台回路控制方法

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043812

Jing Li, G. Zhao, Peijun Yu

引用次数: 0

An Improved Method Based on Deep Reinforcement Learning for Target Searching 一种基于深度强化学习的目标搜索改进方法

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043821

Xiaolong Wei, Xiangsong Huang, Tao Lu, Ge Song

{"title":"An Improved Method Based on Deep Reinforcement Learning for Target Searching","authors":"Xiaolong Wei, Xiangsong Huang, Tao Lu, Ge Song","doi":"10.1109/ICRAE48301.2019.9043821","DOIUrl":"https://doi.org/10.1109/ICRAE48301.2019.9043821","url":null,"abstract":"Unmanned Aerial Vehicle (UAV), due to their high mobility and the ability to cover areas of different heights and locations at relatively low cost, are increasingly used for disaster monitoring and detecting. However, developing and testing UAVs in real world is an expensive task, especially in the domain of search and rescue, most of the previous systems are developed on the basis of greedy or potential-based heuristics without neural network. On the basis of the recent development of deep neural network architecture and deep reinforcement learning (DRL), in this research we improved the probability of success rate of searching target in an unstructured environment by combining image processing algorithms and reinforcement learning methods (RL). This paper aims at the deficiency of target tracking in unstructured environment, trying to propose an algorithm of stationary target positioning of UAV based on computer vision system. Firstly, a new input source is formed by acquiring depth information image of current environment and combining segmentation image. Secondly, the DQN algorithm is used to regulate the reinforcement learning model, and the specific flight response can be independently selected by the UAV through training. This paper utilizes open-source Microsoft UAV simulator AirSim as training and test environment based with Keras a machine learning framework. The main approach investigated in this research is modifying the network of Deep Q-Network, which designs the moving target tracking experiment of UAV in simulation scene. The experimental results demonstrate that this method has better tracking effect.","PeriodicalId":270665,"journal":{"name":"2019 4th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129413893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Human Motion Prediction Based on Visual Tracking 基于视觉跟踪的人体运动预测

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043816

Juncheng Zou, Weihua Yin, E. X. Wang, Jiancheng Wang, Yan-Feng Lu

{"title":"Human Motion Prediction Based on Visual Tracking","authors":"Juncheng Zou, Weihua Yin, E. X. Wang, Jiancheng Wang, Yan-Feng Lu","doi":"10.1109/ICRAE48301.2019.9043816","DOIUrl":"https://doi.org/10.1109/ICRAE48301.2019.9043816","url":null,"abstract":"Following and moving according to human motion is an important task for mobile robots. To ensure more compliant motion planning and execution of mobile robots, they can not be controlled by real-time visual information. In the tracking process, robots need to recognize and track the target then, plan the motion and execute the motion. In practical applications, the environments are very complex, such as illumination, shadows and occlusion, which the traditional visual tracking algorithms often deviate or lose the target. Therefore, to achieve fast human motion tracking, it is necessary to predict human motions by video prediction. In this paper, we propose a human motion tracking algorithm of mobile robot based on video prediciton. (1) The multi-layer generation adversarial loop network, which trained by off-line video dataset and learn how to predict human motion. (2) We used the pre-trained model to predict the state of the tracking target in the video. (3) This video prediction model was integrated into human-to-robot tracking algorithms of mobile robot following human system. Experiments show that the proposed algorithm can track human motion at a certain speed and precision.","PeriodicalId":270665,"journal":{"name":"2019 4th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132420441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Sigma-3: Integration and Analysis of a 6 DOF Robotic Arm Configuration in a Rescue Robot Sigma-3:救援机器人六自由度机械臂构型的集成与分析

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043799

R. A. Bindu, A. A. Neloy, S. Alam, N. J. Moni, Shahnewaz Siddique

{"title":"Sigma-3: Integration and Analysis of a 6 DOF Robotic Arm Configuration in a Rescue Robot","authors":"R. A. Bindu, A. A. Neloy, S. Alam, N. J. Moni, Shahnewaz Siddique","doi":"10.1109/ICRAE48301.2019.9043799","DOIUrl":"https://doi.org/10.1109/ICRAE48301.2019.9043799","url":null,"abstract":"This paper introduces a rescue robot named Sigma-3 which is developed for potential applications such as helping hands for humans where a human can't reach to have an assessment of the hazardous environment. Also, these kinds of robot can be controlled remotely with an adequate control system. The proposed methodology forces on two issues - 1. Novel mechanism design for measuring rotation, joints, links of Degree of Freedom (DOF) for an arm which is integrated with Sigma-3 2. Precise measuring of end-effector motion control over three dimensions. In the proposed mechanism design, the DOF measurement is presented by a planar and spatial mechanism where 4 types of rigid joints build up each DOF with controlling by six High Torque MG996R servo motors. Rotation and DOF measurement are consisting of different theoretical references of Rotation Matrix, Inverse Kinematics with experimental results. Presented methodology over Oscillation Damping performance exhibits less than 3% error while configuring for on hands testing. Another evaluation of operating time state strongly defends the mechanism of low power consumption ability.","PeriodicalId":270665,"journal":{"name":"2019 4th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122206882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Method Combining Machine Vision and Machine Learning for Reed Positioning in Automatic Aerophone Manufacturing 航空器自动制造中簧片定位的机器视觉与机器学习相结合的方法

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043784

Xu Jie, Qin Kailin, Xu Yuanhao, Ji Weixi

{"title":"Method Combining Machine Vision and Machine Learning for Reed Positioning in Automatic Aerophone Manufacturing","authors":"Xu Jie, Qin Kailin, Xu Yuanhao, Ji Weixi","doi":"10.1109/ICRAE48301.2019.9043784","DOIUrl":"https://doi.org/10.1109/ICRAE48301.2019.9043784","url":null,"abstract":"The free reed aerophone, such as accordion, harmonica and melodica, is one of the most popular categories of music equipment in the world. The key operation of the free reed aerophone manufacturing process is to weld multiple reeds onto the reed frame precisely and quickly. In this paper, we propose a method combining machine vision and machine learning algorithms to assist the mechanical device to estimate adjusting displacement and to determine the correctness of the reed positioning operation. Images of reeds on frames are captured and processed, and then some novel features are defined and extracted. Classification models and regression models such as artificial neural network (ANN), support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN) and linear regression (LR) are applied and trained to estimate if the reed position is correct and to measure the adjusting displacement if necessary. It is found that the Back propagation neural network (BPNN) presents 100% accuracy for the correctness estimation and $pm 0.025mathrm{mm}$ measuring precision.","PeriodicalId":270665,"journal":{"name":"2019 4th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125868738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Sim-to-real: Six-legged Robot Control with Deep Reinforcement Learning and Curriculum Learning 模拟到真实:基于深度强化学习和课程学习的六足机器人控制

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043822

Bangyu Qin, Yue Gao, Y. Bai

{"title":"Sim-to-real: Six-legged Robot Control with Deep Reinforcement Learning and Curriculum Learning","authors":"Bangyu Qin, Yue Gao, Y. Bai","doi":"10.1109/ICRAE48301.2019.9043822","DOIUrl":"https://doi.org/10.1109/ICRAE48301.2019.9043822","url":null,"abstract":"Six-Iegged robots have higher stability and balance, which helps them face more complex terrain conditions, such as sand, swamp, mine and so forth. Therefore, it is necessary to study the gait planning of six-legged robot to adapt to complex terrain. In order to control six-legged robots to adapt to different terrains, we adopt the method of deep reinforcement learning (DRL) to plan the gait of six-legged robots. The main idea is training the robot through Actor-Critic network with proximal policy optimization (PPO), in which outputs are step length, step height and orientation of the robot. This is an end-to-end approach, which tries to make the robot learn by itself and finally achieve its safe arrival to the target point through complex terrains. In order to train a good model for our robots, simplified environment is adopted to accelerate the training process. We also use curriculum learning to speed up and optimize the training. Then, we verify the reliability of the method in simulation platform and finally transfer the learned model to real robot. Our experiment shows the effectiveness of deep reinforcement learning for locomotion of six-legged robots, the acceleration of the training process by means of curriculum learning, and the improvement of the training effect.","PeriodicalId":270665,"journal":{"name":"2019 4th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115481323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Aerodynamic Investigation on a Novel Paper-Folding Micro Annular Aerial Robot 新型折纸微型环形航空机器人气动特性研究

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043792

Min Chang, Xiaoyu Feng, J. Bai, Yu Zheng

引用次数: 0

Friction from Reflectance: Transfer Learning Approach 来自反思的摩擦:迁移学习方法

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043793

Piotr Kicki, K. Walas

引用次数: 0

Gait Analysis of Multi-Step Climbing Active Wheeled Snake Robot 多步爬行主动轮式蛇形机器人的步态分析

2019 4th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2019-11-01 DOI: 10.1109/ICRAE48301.2019.9043828

Divyanshu Sahu, R. Kodi, Sartaj Singh

引用次数: 0