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Design of a PC-based Open Industrial robot Control System Integrated with Real-Time Machine vision 1

Design of a PC-based Open Industrial Robot Control System In...

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1st World robot conference (WRC) / Symposium on Advanced robotics and Automation (SARA)

作者： Liu, Yanan Hu, Tianliang Ni, Hepeng Zhang, Chengrui Lin, Bao Judd, Euan Univ Bristol Bristol Robot Lab Bristol BS16 1QY Avon England Shandong Univ Sch Mech Engn Jinan 250061 Shandong Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan 250061 Shandong Peoples R China

ISBN: (纸本)9781538676752

This paper presents a new industrial robot control system integrated with real-time image processing based on a Windows PC. This enables both machine vision and robot control tasks to be carried out on a single general-purpose computer rather than using two separate systems to perform different tasks. To accomplish this, a three-layer software structure is developed along with a series of standard peripheral devices in the hardware layout. Real-time performance of the system is tested and we demonstrate the system on a DELTA parallel manipulator. Experimental results show that the manipulator can pick up 120 objects on a moving conveyor per minute. This work demonstrates the feasibility of integrating real-time machine vision tasks with robot control tasks without dedicated hardware.

关键词： Real-time systems Hardware robot control Machine vision Microsoft Windows standards

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Disparity-Based Robust Unstructured Terrain Segmentation 1st

Disparity-Based Robust Unstructured Terrain Segmentation

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1st Chinese conference on Pattern Recognition and computer vision (PRCV)

作者： Zhang, Pengbo Ma, Xinzhu Wang, Zhihui Li, Haojie Luo, Zhongxuan Dalian Univ Technol 321 Tuqiang St Dalian Liaoning Peoples R China

ISBN: (纸本)9783030033415;9783030033408

Autonomous robot navigation in unstructured outdoor environment is still a challenging problem, and the terrain segmentation is one of the key tasks in robot navigation. Previous methods work well on common terrains like urban roads, but tend to fail in wild conditions due to different illumination, weather and road variations. In this paper, we propose a novel two branches terrain segmentation network based on disparity map and ground plane fitting, introducing geometric characteristics into the network. The terrain segmentation main branch uses convolutional feature layers with multiple sampling rates filters, which effectively considers local and global context information and smooths the holey information in the disparity map. The enhancement branch exploits plane geometry property of the ground plane deviation map calculated from the disparity map, which adaptively generates reference feature maps for improving the robustness of identifying traversable areas under conditions of unseen terrains. Experimental results demonstrate excellent performance of the proposed method on terrain segmentation both qualitatively and quantitatively.

关键词： Terrain segmentation Disparity-based Plane geometry property Convolutional neural network

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Autonomous Underwater Vehicle for vision Based Tracking 1

Autonomous Underwater Vehicle for Vision Based Tracking

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1st International conference on robotics and Smart Manufacturing (RoSMa)

作者： Kumar, G. Santhan Painumgal, Unnikrishnan V. Kumar, M. N. V. Chaitanya Rajesh, K. H. V. Vignans Inst Informat Technol VSEZ Vadlapudi Post Visakhapatnam 530049 Andhra Pradesh India

This paper describes about the design, construction and control of an "Autonomous Underwater Vehicle for vision Based Tracking", built in Vignan's Institute of Information Technology, Centre for Innovation Lab. The underwater vision robot is essentially an Autonomous Underwater Vehicle (AUV) for study of marine animals by automatically tracking and following them using computer vision. The AUV can also be used as a platform for Photo-mapping of the Seafloor using the onboard camera and light arrangement. The robot is proposed to have 5 thrusters configurations to achieve 4 degrees of freedom controlled by an Inertial Measurement Unit (IMU) interfaced with control unit and powered by commercial LiPo battery packs. The robot is equipped with roll, pitch, heading, and depth sensors which provide sufficient feedback signals to automatically control the vehicle to track a pre-planned trajectory. The centre of gravity and centre of buoyancy of the vehicle are positioned in such a way that it is self-stabilized. Along with this the combinations of sensors and speed control drivers provide more stability to the system using a closed loop control system, without the operator involvement. The AUV also captures videos during its mission using the camera. It is planned to have a multi-core umbilical cable for video signal, water leakage alarm, feedback signals and battery charging lines. This will be only used for development and test purposes and will be removed during autonomous missions. Various control schemes can be applied for the vehicle to track different paths. The AUV is designed to the dimension of 575x210x175mm. The AUV uses O-rings for the hulls for good water sealing effect as well as for faster assembly and disassembly. We expect this AUV development to mature in to an advanced system can be used as a platform for study of the ocean by scientists, for environmental studies and for defense applications. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Autonomous Underwater Vehicle vision Based Control System Image Tracking and Processing

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Synchronized video and motion capture dataset and quantitative evaluation of vision based skeleton tracking methods for robotic action imitation 1st

Synchronized video and motion capture dataset and quantitati...

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1st International conference on Information and Communication Technology for Development for Africa, ICT4DA 2017

作者： Atnafu, Selamawet Nicola, Conci Bahir Dar Institute of Technology Bahir Dar Ethiopia University of Trento Trento Italy

ISBN: (纸本)9783319951522

Marker-less skeleton tracking methods are being widely used for applications such as computer animation, human action recognition, human robot collaboration and humanoid robot motion control. Regarding robot motion control, using the humanoid’s 3D camera and a robust and accurate tracking algorithm, vision based tracking could be a wise solution. In this paper we quantitatively evaluate two vision based marker-less skeleton tracking algorithms (the first, Igalia’s Skeltrack skeleton tracking and the second, an adaptable and customizable method which combines color and depth information from the Kinect.) and perform comparative analysis on upper body tracking results. We have generated a common dataset of human motions by synchronizing an XSENS 3D Motion Capture System, which is used as a ground truth data and a video recording from a 3D sensor device. The dataset, could also be used to evaluate other full body skeleton tracking algorithms. In addition, sets of evaluation metrics are presented. © 2018, ICst Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Anthropomorphic robots

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1st International conference on Information and Communication Technology for Development for Africa, ICT4DA 2017

1st International Conference on Information and Communicatio...

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1st International conference on Information and Communication Technology for Development for Africa, ICT4DA 2017

ISBN: (纸本)9783319951522

The proceedings contain 31 papers. The special focus in this conference is on Information and Communication Technology for Development for Africa. The topics include: Developing knowledge based recommender system for tourist attraction area selection in Ethiopia: A case based reasoning approach;a corpus for amharic-english speech translation: The case of tourism domain;experimenting statistical machine translation for ethiopic semitic languages: The case of Amharic-Tigrigna;synchronized video and motion capture dataset and quantitative evaluation of vision based skeleton tracking methods for robotic action imitation;ethiopian public universities’ web site usability;comparative analysis of moving object detection algorithms;Multiple antenna (MA) for cognitive radio based wireless mesh networks (CRWMNs): Spectrum sensing (SS);the design and the use of knowledge management system as a boundary object;autonomous flyer delivery robot;a finite-state morphological analyzer for wolaytta;minimal dependency translation: A framework for computer-assisted translation for under-resourced languages;Massive MIMO for 5G cellular networks: Potential benefits and challenges;mathematical modeling and dynamic simulation of gantry robot using bond graph;web usage characterization for system performance improvement;critical success factors and key performance indicators for e-government projects- Towards untethered public services: The case of Ethiopia;intelligent license plate recognition;comparison of moving object segmentation techniques;towards group fuzzy analytical hierarchy process;overview of spectrum sharing models: A path towards 5G spectrum toolboxes;Towards affordable broadband communication: A quantitative assessment of TV white space in Tanzania;malaria detection and classification using machine learning algorithms;review on cognitive radio technology for machine to machine communication.

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vision Based Intelligent Vehicle steering Control Using Single Camera for Automated Highway System 1

Vision Based Intelligent Vehicle Steering Control Using Sing...

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1st International conference on robotics and Smart Manufacturing (RoSMa)

作者： Manivannan, P. V. Ramakanth, Pulidindi IIT Madras Dept Mech Engn Chennai 600036 Tamil Nadu India

Generally, stereoscope vision is used for guiding autonomous intelligent road vehicles, as image depth can be calculated easily. However, when one of the camera fails, it will advantages to have suitable guidance algorithm that can detect the lane marking using single camera. The primary objective of this work is to develop and implement control algorithms for identifying and guiding the intelligent road vehicle in the assigned lane using image-processing techniques, using single camera. It deals with dividing the video being taken by the camera, into several number of frames. Then, the obtained frames are processed using Image acquisition techniques in Matlab. It detects the lane to be followed through image processing, calculates the angle of movement required for the robot to stay in the assigned path and commands the steering system with appropriate control algorithms. The identification of lane is done using color detection and boundary marking techniques. The image initially undergoes Inverse Perspective Mapping (IPM) for viewing the 3-D space in a 2-dimensional array. Once the IPM of the acquired image undergoes lane detection algorithm, the angle of any curve in the lane is detected using angle detection module. The robot is also made to move at the center of the path using Fuzzy Logic algorithms, where it takes distance from the left detected edge as the input and outputs the angle to be moved by the robot to stay in the center. The developed algorithm has been tested using P3-DX Pioneer mobile in laboratory condition. The experimental result obtained show that the algorithm used by the vehicle to follow the center of the lane, with any given angle or position of initiation, has worked all the times under given experimental conditions, with a maximum error of 2cm. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Automated Highway System Intelligent Vehicle Inverse Perspective Mapping Fuzzy Logic

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Image Segmentation Based on Semantic Knowledge and Hierarchical Conditional Random Fields 1st

Image Segmentation Based on Semantic Knowledge and Hierarchi...

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1st Chinese conference on Pattern Recognition and computer vision (PRCV)

作者： Qin, Cao Zhang, Yunzhou Hu, Meiyu Chu, Hao Wang, Lei Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783030033989;9783030033972

Semantic segmentation is a fundamental and challenging task for semantic mapping. Most of the existing approaches focus on taking advantage of deep learning and conditional random fields (CRFs) based techniques to acquire pixel-level labeling. One major issue among these methods is the limited capacity of deep learning techniques on utilizing the obvious relationships among different objects which are specified as semantic knowledge. For CRFs, their basic low-order forms cannot bring substantial enhancement for labeling performance. To this end, we propose a novel approach that employs semantic knowledge to intensify the image segmentation capability. The semantic constraints are established by constructing an ontology-based knowledge network. In particular, hierarchical conditional random fields fused with semantic knowledge are used to infer and optimize the final segmentation. Experimental comparison with the state-of-the-art semantic segmentation methods has been carried out. Results reveal that our method improves the performance in terms of pixel and object-level.

关键词： Image segmentation Semantic knowledge Ontology Conditional random fields

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Design and Development of Intelligent AGV Using computer vision and Artificial Intelligence 1st

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1st International conference on Soft Computing: Theories and Applications, SoCTA 2016

作者： Sheth, Saurin Ajmera, Anand Sharma, Arpit Patel, Shivang Kathrecha, Chintan Mechatronics Department G H Patel College of Engineering and Technology Gujarat India

ISBN: (纸本)9789811056864

The main aim of this paper is to develop a smart material handling system using an AGV (automated guided vehicle). The task is to transport a container of a fixed size from a defined start point to a defined end point. There is an overhead camera located at the boundary of the arena, in such a way that complete arena can be seen in a single frame. The camera will be capturing real-time images of the vehicle to determine its position and orientation using OpenCV library. The computer will also perform the task of path planning by using various artificial intelligence algorithms like RRT (rapidly random exploring tree) and A* (A star). The outcome of this process will be the shortest path from beginning point to finish point while avoiding the obstacles. The commands should be enough for the robot to understand where it should go next, i.e., the next pose for the robot. This process continues until the goal is reached. To achieve this, few algorithms are developed for shape detection and edge detection. They help in determining the obstacles and the free area/ path where robot can traverse. The image from overhead camera is used to make the shortest global path from start to end using image processing. The computer will do this using various packages in ROS (robot operating system). This global path will generate waypoints for robot to traverse and the image will also provide current pose for the robot. Though the orientation of the obstacles varies the path of AGV and will always follow the shortest path. Thus, AGV shows the artificial intelligent. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Cameras

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A Hierarchical Cluster Validity Based Visual Tree Learning for Hierarchical Classification 1st

A Hierarchical Cluster Validity Based Visual Tree Learning f...

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1st Chinese conference on Pattern Recognition and computer vision (PRCV)

作者： Zheng, Yu Fan, Jianping Zhang, Ji Gao, Xinbo Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

ISBN: (纸本)9783030033385;9783030033378

For hierarchical learning, one open issue is how to build a reasonable hierarchical structure which characterize the inter-relation between categories. An effective approach is to utilize hierarchical clustering to build a visual tree structure, however, the critical issue of this approach is how to determine the number of clusters in hierarchical clustering. In this paper, a hierarchical cluster validity index (HCVI) is developed for supporting visual tree learning. Before clustering of each level begins, we will measure the impact of different numbers of clusters on visual tree building and select the most suitable number of clusters. The proposed HCVI will control the structure of visual tree neither too flat nor too deep. Based on this visual tree, a hierarchical classifier can be trained for achieving more discriminative capability. Our experimental results have demonstrated that the proposed hierarchical cluster validity index (HCVI) can guide the building of a more reasonable visual tree structure, so that the hierarchical classifier can achieve better results on classification accuracy.

关键词： Hierarchical cluster validity Number of clusters Visual tree Hierarchical classification

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Survey on Detection and Tracking of UAVs Using computer vision 1

Survey on Detection and Tracking of UAVs Using Computer Visi...

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1st IEEE International conference on robotic Computing (IRC)

作者： Wagoner, Amy R. Schrader, Daniel K. Matson, Eric T. Purdue Univ Lab M2M Comp & Informat Technol W Lafayette IN 47907 USA

Small unmanned aerial vehicles (UAVs) have become increasingly popular in the last several years. This paper explores numerous methods to detect and track small UAVs using computer vision.

ISBN: (纸本)9781509067244

Small unmanned aerial vehicles (UAVs) have become increasingly popular in the last several years. This paper explores numerous methods to detect and track small UAVs using computer vision.

关键词： Feature extraction computer vision Hidden Markov models Cameras Shape Object detection Target tracking

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