Currently, our main and most important concern is a dynamic and fast walk. After investigating different methods and algorithms by Baset Kid Size, we decided to use an Omni-direction based method that can process with different speeds and has been created and developed by Baset Teen-Size. The major improvement of this module is using a trajectory learning approach that was trained on NAO robot in simulation In this module, hands are used to increase dynamic and speed, and to prevent any decrease in stability. In addition, we slightly modified this module and added a balance control system through force sensitive resistors to improve the performance of this module.
Method of identifying the ball and the elements of the ground is based on the lookup table (LUT) approach. In the first stage the performances of semantic segmentation networks trained for official ball of RoboCup were evaluated and then rank them by label and then learned to designed algorithm. We compare real-time and run-time performance of various networks with each other and with a base classification method to prevent excessive use of central processor. This method directly compares pixel values to trained classes. Forming a table, a support vector (SVM) were learned in the same CNN complex to classify pixels. We also use image inputs in HSV color space and searching in trained network to optimize ball detection and ground elements performance. The result of this training and tests was detecting ball was between 90 to 95 accuracy in different part of the ground
In humanoid League in last year, different algorithms were used which included Extended Kalman filter, particles filter, and Rao-Blackuzelize. In order to estimation absolute current position of the robot in this field, Monte-Carlo localization was used. The robot was benefited from Gyroscope module to estimate (x, y, phi).
Attached file →Before Kish Robocup-ap2018 Tournament, Iran, our strategy and purpose in competitions were briefly catching the ball, moving toward it, finding gate, adjusting angle of standing and shooting ball. The strategy caused always robot select the shortest direction to reach the ball; but considering that robot moves to shoot regardless the direction of target, in most cases either scoring position was provided for the opponent or before shooting, the opponent's robot was blocking the ball rout and the opportunity was lost. So we alter priorities and equivalence some of them and also using the new localization system, change our strategy as the oval movement towards the ball. Actually, robot in this movement considered shooting direction and try to choose the shortest elliptic direction to reach the ball and keep moving. This way shortened decision-making time for shooting and in fast-moving cases, the robot could be an interesting strategy than before.
champion of Iran Open 2017, in Teen Size Humanoid league. Participate in Robocup 2017 Japan Participate in Robocup-ap2017 Thailand Participate in Robocup-ap2018 Iran