scholarly journals CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

2020 ◽  
Vol 13 (6) ◽  
pp. 442-453
Author(s):  
Rudy Dikairono ◽  
◽  
Setiawardhana Setiawardhana ◽  
Djoko Purwanto ◽  
Tri Sardjono ◽  
...  
Keyword(s):  
Intelligent System ◽  
The Self ◽  
Soccer Robot ◽  
Localization System ◽  
System A ◽  
Modelling Method ◽  
Similar Images ◽  
Accuracy Rates ◽  
Soccer Robots ◽  
Soccer Field

The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

A NOVEL ENDOBRONCHIAL FLUOROSCOPIC NAVIGATION AND LOCALIZATION SYSTEM: A SUMMARY OF A MULTICENTER LUNGVISION TRIAL

CHEST Journal ◽  
2018 ◽  
Vol 154 (4) ◽  
pp. 880A-882A ◽  
Author(s):  
D. HOGARTH ◽  
KRISH BHADRA ◽  
PATRICK WHITTEN ◽  
MICHAEL PRITCHETT
Keyword(s):  
Localization System ◽  
System A ◽  
Fluoroscopic Navigation

scholarly journals A Study on Development of Engine Fault Diagnostic System

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Hwa-seon Kim ◽  
Seong-jin Jang ◽  
Jong-wook Jang
Keyword(s):  
Data Transmission ◽  
Diagnostic System ◽  
The Self ◽  
System Management ◽  
Diagnostic Code ◽  
Quick Response ◽  
System A ◽  
Message Structure ◽  
To Receive ◽  
Engine Condition

This study implemented a mobile diagnosing system that provides user-centered interfaces for more precisely estimating and diagnosing engine conditions through communications with the self-developed ECU only for industrial CRDI engine use. For the implemented system, a new protocol was designed and applied based on OBD-II standard to receive engine data values of the developed ECU. The designed protocol consists of a message structure to request data transmission from a smartphone to ECU and a response message structure for ECU to send data to a smartphone. It transmits 31 pieces of engine condition information simultaneously and sends the trouble diagnostic code. Because the diagnostic system enables real-time communication through modules, the engine condition information can be checked at any time. Thus, because when troubles take place on the engine, users can check them right away, quick response and resolution are possible, and stable system management can be expected.

Guiding Self-Organization in Systems of Cooperative Mobile Agents

2011 ◽  
pp. 268-290
Author(s):  
Alejandro Rodríguez ◽  
Alexander Grushin ◽  
James A. Reggia
Keyword(s):  
Swarm Intelligence ◽  
Self Assembly ◽  
The Self ◽  
Self Organization ◽  
Assembly System ◽  
Global Behavior ◽  
System A ◽  
Collective Transport ◽  
Reactive Behavior ◽  
Different Levels

Drawing inspiration from social interactions in nature, swarm intelligence has presented a promising approach to the design of complex systems consisting of numerous, simple parts, to solve a wide variety of problems. Swarm intelligence systems involve highly parallel computations across space, based heavily on the emergence of global behavior through local interactions of components. This has a disadvantage as the desired behavior of a system becomes hard to predict or design. Here we describe how to provide greater control over swarm intelligence systems, and potentially more useful goal-oriented behavior, by introducing hierarchical controllers in the components. This allows each particle-like controller to extend its reactive behavior in a more goal-oriented style, while keeping the locality of the interactions. We present three systems designed using this approach: a competitive foraging system, a system for the collective transport and distribution of goods, and a self-assembly system capable of creating complex 3D structures. Our results show that it is possible to guide the self-organization process at different levels of the designated task, suggesting that self-organizing behavior may be extensible to support problem solving in various contexts.

The STECC Framework - An Architecture for Self-Testable Components

2007 ◽  
pp. 213-251 ◽  
Author(s):  
Sami Beydeda
Keyword(s):  
The Self ◽  
Software System ◽  
Exchange Of Information ◽  
System A ◽  
Lack Of Information ◽  
Self Testing ◽  
Technical Architecture ◽  
New Form ◽  
Limited Exchange

Development of a software system from existing components can surely have various benefits, but can also entail a series of problems. One type of problem is caused by a limited exchange of information between the developer and user of a component. A limited exchange and thereby a lack of information can have various consequences, among them the requirement to test a component prior to its integration into a software system. A lack of information cannot only make testing prior to integration necessary; it can also complicate this task. However, difficulties in testing can be avoided if certain provisions to increase testability are taken beforehand. This article briefly describes a new form of improving testability of, particularly commercial, components, the self-testing COTS components (STECC) strategy and explains in detail the STECC framework, which implements the necessary technical architecture to augment Java components with self-testability.

LIE GROUP FORMULATION FOR ANALYSIS OF KICKING MOTION IN HUMANOID SOCCER ROBOTS

2008 ◽  
Vol 05 (03) ◽  
pp. 501-522
Author(s):  
LIANDONG ZHANG ◽  
CHANGJIU ZHOU
Keyword(s):  
Dynamic Stability ◽  
Lie Group ◽  
Soccer Robot ◽  
Zero Moment Point ◽  
Zero Moment ◽  
Compensation Approach ◽  
Soccer Robots

In this paper, we develop a general Lie group framework for analysis of kicking motion in humanoid soccer robots which aims to capture the multidimensional kicking information and hence to study how to develop more powerful and skilful kicking motion for humanoid soccer robots. To maintain dynamic stability while kicking is being performed, the zero-moment point (ZMP) is used to evaluate the performance of the humanoid kick. The proposed Lie-group-formulation-based compensation approach for force/torque sensing from the humanoid ankle has been implemented on, Robo-Erectus, our humanoid soccer robot. Humanoid kicking experiments have been conducted to verify the effectiveness of the proposed approach.

scholarly journals Self-reactions in the HCl+ (DCl+) + HCl system: a state-selective investigation of the role of rotation

2015 ◽  
Vol 17 (25) ◽  
pp. 16454-16461 ◽  
Author(s):  
Till Uhlemann ◽  
Jens Wallauer ◽  
Karl-Michael Weitzel
Keyword(s):  
Cross Sections ◽  
Rotational Velocity ◽  
The Self ◽  
System A ◽  
The Cross

The cross sections for the self-reaction of state-selected HCl+ (DCl+) ions with HCl are shown to depend characteristically on the rotational velocity of the ion relative to that of the neutral.

Intelligent Hybrid System: A Reliability-Based Failure Management Application

2001 ◽  
Vol 5 (6) ◽  
pp. 307-314 ◽  
Author(s):  
Jose Aguilar ◽  
◽  
Mariela Cerrad ◽  
Katiuska Morillo ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Historical Data ◽  
Intelligent System ◽  
The Other ◽  
Hybrid Architecture ◽  
System A ◽  
Reliability Centered Maintenance ◽  
Management Application

The integration of different intelligent techniques (such as Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms, etc.) into a hybrid architecture allows to overcome their individual limitations. In industrial environments, these intelligent techniques can be combined to reach more effective solutions to complex problems. On the other hand, failure management in processes, equipment or plants, acquires more importance in modern industry every day, in order to minimize unexpected faults and guaranties a greater reliability, safety, disposition and productivity in the industry. In this paper, an intelligent system is designed for failure management based on Reliability Centered Maintenance methodology, Fuzzy Logic and Neural Networks. The system proposes the maintenance tasks according to the historical data of the equipment.

Mono-Camera-Based Robust Self-Localization Using LIDAR Intensity Map

2020 ◽  
Vol 32 (3) ◽  
pp. 624-633
Author(s):  
Kei Sato ◽  
Keisuke Yoneda ◽  
Ryo Yanase ◽  
Naoki Suganuma ◽  
◽  
...  
Keyword(s):  
Template Matching ◽  
Low Cost ◽  
The Self ◽  
Light Detection And Ranging ◽  
Automated Vehicles ◽  
Light Detection ◽  
Localization Method ◽  
Localization System ◽  
Intensity Map ◽  
Map Data

An image-based self-localization method for automated vehicles is proposed herein. The general self-localization method estimates a vehicle’s location on a map by collating a predefined map with a sensor’s observation values. The same sensor, generally light detection and ranging (LIDAR), is used to acquire map data and observation values. In this study, to develop a low-cost self-localization system, we estimate the vehicle’s location on a LIDAR-created map using images captured by a mono-camera. The similarity distribution between a mono-camera image transformed into a bird’s-eye image and a map is created in advance by template matching the images. Furthermore, a method to estimate a vehicle’s location based on the acquired similarity is proposed. The proposed self-localization method is evaluated on the driving data from urban public roads; it is found that the proposed method improved the robustness of the self-localization system compared with the previous camera-based method.

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