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opencc-by-4.0Apr 2010View details →

Figure 8. Artificial Intelligence Algorithm-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

This module receives information from Artificial Intelligent unit. Total functions about Robot Behavior such as stability motors actions, robot path planning, turn camera, walking, shooting, dribbling; motion and etc are controlled in this section.

opencc-by-4.0Apr 2010View details →

Figure 1. PERSIA Humanoid Robot in Robocup IranOpen2010 Competition-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

In this paper, we will at first describe the general hardware design of the PERSIA Humanoid Robocup Team, (section 2) and after that focus on our scientific approaches in sensor fusion and learning (section 3). Finally, section 4 concludes this paper. This document describes the current state of the project as well as the intended development for the RoboCup 2010 competition.

opencc-by-4.0Apr 2010View details →

Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

The PERSIA Humanoid robot designed for has multipurpose capability. This robot equipped with main board for motion control, vision sensor, other balancing sensors, servo motors and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control system.

Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

We have presented the system for a fully autonomous navigation of an UAV based on Omni directional vision system and image processing. we explain vision system configuration ,image processing and feature extraction methods and finaly suggest an algorithm based on potential field for navigation of an UAV.

Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

The numerical potential field path planner is guaranteed to produce a path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start to the goal without passing through an obstacle then the path planner will generate a path through the obstacle, although if there is any alternative then the path will do that instead. For this reason, it is important to make sure that there is some possible path, although there are ways around this restriction such as returning an error if the potential at the start point is too high. The path is found by moving to the neighboring square with the lowest potential, starting at any point in the space and stopping when the goal is reached.

Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the motion trajectory of UAV is divided into several median points that the UAV should reach them one by one in a sequence the output obtained after the execution of AI will be a set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through the obstacles to reach the destination. The routine used for this purpose is the potential field method (also an alternative new method is in progress which models the UAV motion through opponents same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be used to anticipate the position of the obstacles and make better decisions in order to reach the desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the value of the potential at all other points is positive.

Figure 5. Images binarized by SVM.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

In instance, the image in Fig. 4 was binarized manually (however, the person in charge of color adjustment is professional) and by using SVM resulting in Fig.5

Figure 4. Images binarized by hand.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

In instance, the image in Fig. 4 was binarized manually (however, the person in charge of color adjustment is professional) and by using SVM resulting in Fig.5

Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System-Figure 3. The principle of SVM

Manual segmentation training was hard and very time consuming and rely to operator's accuracy, so we developed a color calibration algorithm using SVM(Support Vector Machine). In this subsection, we present a color recognition algorithm using the support vector machine (SVM).SVM is one of the classification algorithms which it has high generality since it can calculate a super plane that maximizes the margin of classes, Fig.3.[8] In our algorithm, the SVM is trained by the H'SY values of the classes and the mean of the obtained image H'SY values. After training, the obtained image is binarized by setting the maximum and minimum value in the distribution of each class as a threshold.

Figure 2. Catadioptric projection modelled by the unit sphere-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the beginning of UAV, the map building was one of the most addressed problems by researchers. Several researchers used Omni directional vision for robot navigation and map building. Because of the wide field of view in Omni directional sensors, the robot does not need to look around using moving parts (cameras or mirrors) or turning the moving parts. The global view offered by Omni directional vision is especially suitable for highly dynamic environments. The Omni directional vision system consists of a hyperbolic mirror, a USB color digital camera (Logitech C905) and a regulation device.

Figure 1. The use of visual servo control for helicopter stabilization-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Visual servoing is an approach to control motion of a helicopter using information feedback from a camera mounted on it. For their tremendous potential applications in various areas including environmental monitoring and anti-terrorism, unmanned small helicopters are being extensively studied in robotics and control in recent years. However, the research advance in dynamic control of small helicopters is limited due to highly coupled non- linear dynamics and the existence of various uncertain- ties. Many people studied controller design based on a Publisher Item Identifier. linearized or simplified model, but the controllers developed under linearized models cannot guarantee dynamic stability rigorously. Another effort is application of modern non-linear control theory to helicopter control because small helicopter are good test beds for sophisticated control techniques for their small size and highly coupled dynamics [4].

opencc-by-4.0May 2024View details →

Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios

<h1>Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios</h1> Before being deployed on roads, Autonomous Vehicles (AVs) must undergo comprehensive testing. Safety-critical situations, however, are infrequent in usual driving conditions, so simulated scenarios are used to create them. A test scenario comprises static and dynamic features related to the AV and the test environment; the representation of these features is complex and makes testing a heavy process. A test scenario is effective if it identifies incorrect behaviors of the AV. In this article, we present a technique for identifying the key features of test scenarios associated with their effectiveness using Instance Space Analysis (ISA). ISA generates a ($2D$) representation of test scenarios and their features. This visualization helps to identify combinations of features that make a test scenario effective. We present a graphical representation of each key feature that helps identify how well each testing technique explores the search space. While identifying key features is a primary goal, this study specifically seeks to determine the critical features that differentiate the performance of algorithms. Finally, we present metrics to assess the robustness of testing algorithms and the scenarios generated. Collecting essential features in combination with their values which are associated with effectiveness can be used for selection and prioritization of effective test cases.

Рис. 4. Фиксированные Этанолом Meghimatium bilineatum иЗ бассейна Среднего Амура (вид со спины): А – половоЗрелый, Ярко окраШенный ЭкЗемплЯр, В – молодой, более светлый ЭкЗемплЯр. МасШтабнаЯ линейка – 1 см. Фото Л.А. ПроЗоровой. in First record of the rare slug species Meghimatium bilineatum (Benson, 1842) (Gastropoda: Eupulmonata: Philomycidae) in the Jewish Autonomous Region (Middle Amur basin)

Рис. 4. Фиксированные Этанолом Meghimatium bilineatum иЗ бассейна Среднего Амура (вид со спины): А – половоЗрелый, Ярко окраШенный ЭкЗемплЯр, В – молодой, более светлый ЭкЗемплЯр. МасШтабнаЯ линейка – 1 см. Фото Л.А. ПроЗоровой.

Рис. 3. Фиксированные Этанолом половоЗрелые Meghimatium bilineatum иЗ бассейна Среднего Амура (вид со спины). МасШтабнаЯ линейка – 1 см. Фото Л.А. ПроЗоровой. in First record of the rare slug species Meghimatium bilineatum (Benson, 1842) (Gastropoda: Eupulmonata: Philomycidae) in the Jewish Autonomous Region (Middle Amur basin)

Рис. 3. Фиксированные Этанолом половоЗрелые Meghimatium bilineatum иЗ бассейна Среднего Амура (вид со спины). МасШтабнаЯ линейка – 1 см. Фото Л.А. ПроЗоровой.

opencc-zeroJun 2024View details →

Dataset: Foresight Autonomous Holdings Ltd. (FRSX) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: Global X Autonomous & Electric Vehicles ETF (DRIV) Stock Performance

opencc-by-4.0Dec 2020View details →

Рис. 4. МоΛоΔой вьюн Àабри Paramisgurnus dabrianus (Dabry de Thiersant, 1872) Fig. 4. Young Paramisgurnus dabrianus (Dabry de Thiersant, 1872) in Cobitidae And Balitoridae Of The Middle Part Of Amur In The Jewish Autonomous Region And The Adjacent Border Territories Of China

Рис. 4. МоΛоΔой вьюн Àабри Paramisgurnus dabrianus (Dabry de Thiersant, 1872) Fig. 4. Young Paramisgurnus dabrianus (Dabry de Thiersant, 1872)

Fig. 4 in First record of the rare slug species Meghimatium bilineatum (Benson, 1842) (Gastropoda: Eupulmonata: Philomycidae) in the Jewish Autonomous Region (Middle Amur basin)

Fig. 4. Ethanol preserved Meghimatium bilineatum from the Middle Amur basin (side view): А – mature brightly colored specimen, В – young light-colored specimen. Scale bar – 1 cm. Photo by L.A. Prozorova.

Fig. 1 in First record of the rare slug species Meghimatium bilineatum (Benson, 1842) (Gastropoda: Eupulmonata: Philomycidae) in the Jewish Autonomous Region (Middle Amur basin)

Fig. 1. Locations of the slug Meghimatium bilineatum in the Russian Far East. A new locality in the Jewish Autonomous Region is shown with an asterisk.