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Robot soccer game is one of the significant and interesting areas among most of the autonomous robotic researches. Following the humanoid soccer robot basic movement and strategy actions, the robot is operated in a dynamic and unpredictable contest environment and must recognize the position of itself in the field all the time. Therefore, the local...
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Citations
... Chang et al. [14] proposed an efficient neural network method for achieving self-localization by a humanoid robot. Yang and Cao [15] also proposed a 6D pose estimation of an object using the Levenberg-Marquardt algorithm to refine the result of the decomposed homography matrix. ...
In computer vision, most monovision cameras used for estimating the position of an object only estimate the 2D information of the object without the depth information. Estimating the depth information, which is the distance between the target object and the camera is quite challenging but, in this paper, a less computationally intensive method was used to estimate the object’s distance to complete the 3D information needed to determine the object’s location in cartesian space. In this method, the object was positioned in front of the camera at a sequential distance and was measured directly. The distances measured in the experiment with a set of training data obtained from the image were fitted into a curve using the least-square framework to derive a non-linear function that was used for estimating the object’s distance also known as the z-coordinate. The result from the experiment showed that there was an average error of 1.33 mm between the actual distance and the estimated distance of the object. Hence, this method can be applied in many robotic and autonomous systems applications.
... Hierarchical reactive control for humanoid soccer robot has been proposed by Sven Behnke and Jorg Stuckler [5]. The self-localization based on monocular vision for humanoid robot determines the robot's location using a CCD camera [6]. Vision Based Self Localization for Humanoid Robot Soccer was implemented by Nuryono Satya Widodo and Arif Rahman [7] to create a robot soccer localization system. ...
The algorithm of location and position determination was designed for humanoid soccer robot. The robots have to be able to control the ball effectively on the field of Indonesian Robot Soccer Competition which has a size of 900 cm x 600 cm. The algorithm of location and position determination uses parameters, such as the goalpost's thickness, the compass value, and the robot's head servo value. The goalpost's thickness is detected using The Centre of Gravity method. The width of the goalpost detected is analyzed using the principles of camera geometry to determine the distance between the robot and the goalpost. The tangent value of head servo's tilt angle is used to determine the distance between the robot and the ball. The distance between robot-goalpost and the distance between robot-ball are processed with the difference of head servo's pan angle and compass value using trigonometric formulas to determine the coordinates of the robot and the ball in the Cartesian coordinates.
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In the traditional color adjustment approach, people tried to separately adjust the luminance and saturation. This approach makes the color over-saturate very easily and makes the image look unnatural. In this study, we try to use the concept of exposure compensation to simulate the brightness changes and to find the relationship among luminance, saturation, and hue. The simulation indicates that saturation changes withthe change of luminance and the simulation also shows there are certain relationships between color variation model and YCbCr color model. Together with all these symptoms, we also include the human vision characteristics to propose a new saturation method to enhance the vision effect of an image. As results, the proposed approach can make the image have better vivid and contrast. Most important of all, unlike the over-saturation caused by the conventional approach, our approach prevents over-saturation and further makes the adjusted image look natural.
... Currently most of the color adjustment methods adjust luminance, saturation, and hue separately to find the best performance of each item to finish the color adjustment. However, according to the HVS when the brightness of color changes, it does not simply change luminance, saturation, and hue, separately [12]. Actually the color changes by complying with the changes of luminance, saturation, and hue, and luminance, saturation, and hue must be associated with each other. ...
This paper proposes a saturation adjustment method based on human vision with YCbCr color model characteristics and luminance changes. In the traditional color adjustment approach, people tried to separately adjust the luminance and saturation. However, this approach makes the color over-saturate very easily and makes the image look unnatural. In this work, we try to use the concept of exposure compensation to simulate the brightness changes and to find the relationship among luminance, saturation, and hue. The simulation indicates that saturation changes with the change of luminance and the simulation also shows there are certain relationships between color variation model and YCbCr color model. Together with all these symptoms, we also include the human vision characteristics to propose a new saturation method to enhance the vision effect of an image. The experimental results show that the proposed approach can make the image have better vivid and contrast. Most important of all, unlike the over-saturation caused by the conventional approach, our approach prevents over-saturation and further makes the adjusted image look natural.
This work proposes a new Stereo Vision-Based Self- Localization System (SVBSLS) for the RoboCup soccer humanoid league rules for the 2010 competition. The humanoid robot integrates the information from the pan/tilt motors and stereo vision to accomplish the self-localization and measure the distance of the robot and the soccer ball. The proposed approach uses the trigonometric function to find the coarse distances from the robot to the landmark and the robot to the soccer ball, and then it further adopts the artificial neural network technique to increase the precision of the distance. The statistics approach is also used to calculate the relationship between the humanoid robot and the position of the landmark for self-localization. The experimental results indicate that the localization system of SVBSLS in this research work has 100% average accuracy ratio for localization. The average error of distance from the humanoid soccer robot to the soccer ball is only 0.64 cm.
