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A Real-Time Monocular vision-Based Obstacle Detection

A Real-Time Monocular Vision-Based Obstacle Detection

引用

international conference on control, automation and robotics (ICCAR)

作者： Szu-Hong Wang Xiang-Xuan Li Bachelor Program in Interdisciplinary Studies National Yunlin University of Science & Technology Yunlin Taiwan National Yunlin University of Science & Technology Yunlin Taiwan

ISBN: (数字)9781728161396

ISBN: (纸本)9781728161402

In recent years, many robotic obstacle avoidance methods use high-priced laser detection technology, or a duallens camera as a sensor for robot control. In order to reduce costs, this paper presents a real-time obstacle detection system by using a single wide-angle camera. In the robot application, the system can detect various static or dynamic objects in front of the robot body. the proposed system uses a local object based background subtraction method to get the outline of the static/moving object. the adaptive integral image based obstacle detection method without manual adjustment of setting parameters. the obstacle detection system had been implemented by ARM system, the obstacle can be detected on the real-time camera. the frame rate is close to 32 fps with Raspberry pi 3 platform.

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End-to-End Joint Intention Estimation for Shared control Personal Mobility Navigation

End-to-End Joint Intention Estimation for Shared Control Per...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Kavindie Katuwandeniya Jaime Valls Miro Lakshitha Dantanarayana

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Advancements in technology propose a future where systems work collaboratively sharing the same workspace as humans. Navigation is one such crucial aspect of daily life where collaborative technologies can offer major assistance. Ageing population dictates a likely increase in personal mobility devices (PMDs), whilst autonomous cars are bringing intelligent vehicles to the road today. However, in such scenarios the expected assistance can only be given if the device is aware of its user's intention, so that controls can be applied in a tightly collaborative manner. Moreover, they should be robust to different environments, users and mobile platforms. A user driven navigation framework is proposed in this work to complement end-to-end sensing-only solutions to estimate controls as joint intention from vehicle states and user inputs. the solution is proven to be an improvement over similar strategies that rely on exteroceptive data and omit inputs from the driving agent. Furthermore, the developed framework is proven capable of transferring the learning into different environments and mobility platforms using a small amount of training data. Data from the autonomous driving community (Udacity dataset) and other obtained in-house with an instrumented power wheelchair are given to demonstrate the validity of the proposed approach.

关键词： Navigation Estimation Collaboration Wheelchairs Robot sensing systems Neural networks Network architecture

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3D vision for Precision Dairy Farming 6th

3D Vision for Precision Dairy Farming

引用

6th international-Federation-of-Automatic-control (IFAC) conference on Sensing, control and automation Technologies for Agriculture (AGRIcontrol)

作者： O'Mahony, Niall Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Riordan, Daniel Walsh, Joseph Inst Technol Tralee IMaR Res Ctr Tralee Co Kerry Ireland

3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals this paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. this review will include evaluations of the applicability of Time of Flight and Streoscopic vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. these use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning. (C) 2019, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： automation and robotics in Agriculture Sensing and automation in Animal Fanning

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Industrial Challenges when Planning and Preparing Collaborative and Intelligent automation Systems for Final Assembly Stations 24

Industrial Challenges when Planning and Preparing Collaborat...

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24th IEEE international conference on Emerging Technologies and Factory automation (ETFA)

作者： Hanna, Atieh Bengtsson, Kristofer Dahl, Martin Eros, Endre Gotvall, Per-Lage Ekstrom, Mikael Volvo Grp Trucks Operat Res & Technol Dev Gothenburg Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Malardalen Univ Div Intelligent Future Technol Vasteras Sweden

ISBN: (纸本)9781728103037

During the last five decades, automation and robotics have transformed the automotive industry by increasing efficiency and improving the product quality. However, future trucks that will be autonomous, electrical and connected will require a completely new type of flexibility and intelligence in the production systems, especially in the final assembly. To handle the increased complexity of the products, production processes and logistic systems, final assembly must be transformed into collaborative and intelligent automation systems. these systems will include collaborative and deliberative robots (cobots), advanced vision-based control, adaptive safety systems, online optimization and learning algorithms and connected and well-informed human operators. But it will be a huge undertaking to transform current trucks industry such that they can design, implement and maintain large scale collaborative and intelligent automation systems. this paper presents the challenges with current planning and preparation processes for final assembly as well as the requirement and possible solutions for the future processes. An industrial use case at Volvo Trucks based on Sequence Planner and ROS2 is used to evaluate the proposed planning and preparation processes.

关键词： Production planning Production preparation collaborative robot intelligent system

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Self-adaptive Cobots in Cyber-Physical Production Systems 24

Self-adaptive Cobots in Cyber-Physical Production Systems

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24th IEEE international conference on Emerging Technologies and Factory automation (ETFA)

作者： Nogueira, Roberto Reis, Joao Pinto, Rui Goncalves, Gil Univ Porto Fac Engn Res Ctr Syst & Technol SYSTEC Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9781728103037

Absolute automation in certain industries, such as the automotive industry, has proven to be disadvantageous. Robots are fairly capable when performing tasks that are repetitive and demand precision. However, a hybrid solution comprised of the adaptability and resourcefulness of humans cooperating, in the same task, with the precision and efficiency of machines is the next step for automation. Manipulators, however, lack self-adaptability and true collaborative behaviour. And so, through the integration of vision systems, manipulators can perceive their environment and also understand complex interactions. In this paper, a vision-based collaborative proof-of-concept framework is proposed using the Kinect v2, a UR5 robotic manipulator and MATLAB. this framework implements 3 behavioural modes, 1) a Self-Adaptive mode for obstacle detection and avoidance, 2) a Collaborative mode for physical human-robot interaction and 3) a standby Safe mode. these modes are activated with recourse to gestures, by virtue of the body tracking and gesture recognition algorithm of the Kinect v2. Additionally, to allow self-recognition of the robot, the Region Growing segmentation is combined with the UR5's Forward Kinematics for precise, near real-time segmentation. Furthermore, self-adaptive reactive behaviour is implemented by using artificial repulsive action for the manipulator's end-effector. Reaction times were tested for all three modes, being that Collaborative and Safe mode would take up to 5 seconds to accomplish the movement, while Self-Adaptive mode could take up to 10 seconds between reactions.

关键词： Cobots Collaborative robotics Gesture control Industry 4.0 Kinect v2 Obstacle Detection Potential Fields Self-adaptive Universal Robots UR5 vision-based robot

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Binary Spectrum Feature for Improved Classifier Performance

Binary Spectrum Feature for Improved Classifier Performance

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Nalika Ulapane Karthick thiyagarajan Sarath Kodagoda The University of Melbourne Parkville VIC Australia UTS Robotics Institute University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. therefore, no further feature reduction, or feature addition, is to be carried out. then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation's potential for broader usage.

关键词： Training Task analysis Feature extraction Support vector machines Supervised learning Benchmark testing Training data

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Floor Extraction and Door Detection for Visually Impaired Guidance

Floor Extraction and Door Detection for Visually Impaired Gu...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： B. Berenguel-Baeta M. Guerrero-Viu A. Nova J. Bermudez-Cameo A. Perez-Yus J.J. Guerrero Instituto de Investigación en Ingeniería de Aragón Universidad de Zaragoza Zaragoza Spain

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. the combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. this combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. the proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance.

关键词： Cameras three-dimensional displays Sensors Navigation Image color analysis Sensor systems Robot vision systems

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Development of a Millinewton FBG-Based Distal Force Sensor for Intravascular Interventions

Development of a Millinewton FBG-Based Distal Force Sensor f...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Toluwanimi Oluwadara Akinyemi Olatunji Mumini Omisore Wenke Duan Gan Lu Wenjing Du Yousef Alhanderish Yifa Li Lei Wang Research Center for Medical Robotics and MIS Devices Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Application of intravascular catheterization, a vital task used in minimally invasive vascular surgery, has been hindered by lack of distal force sensor with miniaturized size and millinewton sensing capability. thus, development of distal sensor for evaluating tool-vessel force interactions during robot-assisted intravascular interventions remains a research area in minimally invasive surgery. In this study, a millinewton force sensor is developed by integrating optical fibers with bragg grating in an isotonic 3D-printed flexure. the miniaturized sensor is calibrated in an experiment for 1D distal force sensing application in PCI procedures, and performance of the sensor is evaluated against that of direct FBG-pasting method. Results from the study shows that the designed sensor shows a higher repeatability and stability with a millinewton resolution in the flexure compartment. thus, it can be suitably used for distal catheter-tip force sensing during intravascular catheterization.

关键词： Force Robot sensing systems Optical fiber sensors Force sensors Optical fibers Fiber gratings Strain

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vision-based recognition of pedestrian crossing intention in an urban environment 9

Vision-based recognition of pedestrian crossing intention in...

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9th IEEE Annual international conference on Cyber Technology in automation, control, and Intelligent Systems (IEEE-CYBER)

作者： Yang, Biao Ni, Rongrong Changzhou Univ Dept Informat Sci & Engn Gehu Rd 21 Changzhou Jiangsu Peoples R China Changzhou Vocat Inst Text & Garment Energy Management Sect Gehu Middle Rd 53 Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781728107707

Autonomous moving platforms (like self-driving cars and mobile robots) need to understand the behaviors of pedestrians for better performance. Recognizing pedestrians' crossing intention is one of the most critical capabilities of self-driving cars to guarantee safe operations in an urban environment. Some researchers utilize pedestrians' trajectories to identify their crossing intention, whereas ignoring the informative appearance. this work studies two vision cues which reveal pedestrians' crossing intention. A looking/not looking classifier is built to capture eye contacts between pedestrians and self-driving cars. Such eye contacts are considered to be highly related to pedestrians' crossing intention. A crossing/not crossing classifier is designed to catch early actions before pedestrians starting to cross, like limb movements. Data augmentation is used before training both classifiers due to limited training samples. Meanwhile, both classifiers are optimized with focal loss, which can relieve the issue of easy/hard samples. Experimental evaluations verify the effectiveness of the proposed vision cues in recognizing pedestrians' crossing intention. Comparisons with the baseline indicate the superiorities of the proposed strategies in training the two classifiers.

关键词： crossing intention convolutional neural network looking/not looking classifier moving/not moving classifier self-driving car

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Self-intersection Attention Pooling Based Classification for Rock Recognition

Self-intersection Attention Pooling Based Classification for...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Liang Chen Qian Zheng Zhitao Liu Weijie Mao Fulong Lin State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Fudan University Shanghai China General Institute of Design and Research China Railway Engineering Equipment Group Co. LTD Zhengzhou China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Rock recognition is a critical step for intelligent control system in tunnel boring machine (TBM). We build the rock recognition dataset by the visual monitoring system in TBM. After that, we present an attention-fusion network (AFN) for rock recognition classification. Our model is trained with the recognition object (rock) image, and gets the rock attention map through the self-intersection operation. To complete the classification task, we propose the attention block in the attention-fusion classifier, which is effective to fuse and encode the feature and attention in an end-to-end manner. We confirm the effectiveness of the self-intersection operation and attention block by visualized experiment and contrast experiment. At the same time, experimental results of the rock recognition dataset indicate that AFN outperforms state-of-the-art methods, including LSTM (mask-guided), ABN (attention-based), ResNetSO (global-feature), both in accuracy and realtime on the rock recognition tasks.

关键词： Rocks Task analysis Feature extraction Visualization Monitoring Convolution Image recognition

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