RoboCup Middle Size League Soccer Research Articles

Dynamic control of steerable wheeled mobile platforms applied to an eight-wheeled RoboCup Middle Size League soccer robot

In the RoboCup Middle Size League two teams of mobile robots play soccer against each other. During the game, agility, i.e. the ability to quickly change the direction of platform movements, is important to react or anticipate fast on the intention of opponents to efficiently perform maneuvers like ball shielding and interception. Therefore, high accelerations are desired which ideally would ask all wheels to contribute to traction in the target direction. However none of the current omnidirectional wheel-based robots in the league offers such a feature. Each pair of wheels can rotate independently about its suspension axis . The new configuration brings new challenges in control: the platform becomes kinematically nonholonomic due to the kinematic constraints around the pivot axes, but it is shown that in the context of the driving task the controller can keep the wheel configurations such that they can generate a force and torque in the directions needed by the task. Hereby, the restriction to minimize the position-error in its three degrees of freedom with respect to a predefined trajectory is relaxed by taking only the degrees of freedom relevant for the task into consideration. A cascaded control strategy is proposed that combines kinematic and dynamic control and also addresses the control-allocation problem. Compared to a full kinematic approach as typically applied on steerable wheeled systems, 2.3 times higher translational and 1.8 times higher angular velocity are demonstrated. For the translational acceleration and angular acceleration, improvement factors of 2.7 and 3.2 are achieved, respectively. The platform made a successful debut during the RoboCup Portuguese Open 2019, showing the robustness of the proposed approach.

Evaluation for Ball Dribbling Mechanism of RoboCup Middle Size League Soccer Robot in The World Teams

Evaluation for Ball Dribbling Mechanism of RoboCup Middle Size League Soccer Robot in The World Teams

A robust omnidirectional vision sensor for soccer robots

How to make robot vision work robustly under varying lighting conditions and without the constraint of the current color-coded environment are two of the most challenging issues in the RoboCup community. In this paper, we present a robust omnidirectional vision sensor to deal with these issues for the RoboCup Middle Size League soccer robots, in which two novel algorithms are applied. The first one is a camera parameters auto-adjusting algorithm based on image entropy. The relationship between image entropy and camera parameters is verified by experiments, and camera parameters are optimized by maximizing image entropy to adapt the output of the omnidirectional vision to the varying illumination. The second one is a ball recognition method based on the omnidirectional vision without color classification. The conclusion is derived that the ball on the field can be imaged to be an ellipse approximately in our omnidirectional vision, and the arbitrary FIFA ball can be recognized by detecting the ellipse imaged by the ball. The experimental results show that a robust omnidirectional vision sensor can be realized by using the two algorithms mentioned above.

Intelligent Control of Autonomous Soccer Robots Compensating for Missing Information

For autonomous mobile robots, visual information is used to recognize the environment. Although the acquisition of visual information is often disturbed in the real environment, it is necessary for a robot to act appropriately even if information is missing. We compensate for missing information for autonomous mobile robots by using short-term memory (STM) to make robots act appropriately. This method involves short-term memory and action selectors. Short-term memory is constructed based on the model of human memory and the forgetting curve used in cognitive science. These action selectors use compensated-for information and determine suitable action. One action selector consists of a neural network whose connection weights are learned by a genetic algorithm. Another selector is designed based on the designer's knowledge. These action selectors are switched based on reliability index of information. RoboCup Middle Size League soccer robots are used for demonstration. The experimental and simulation results show its effectiveness.