RoboCup Middle Size League Research Articles

Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System

The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the “Skills, Tactics and Plays (STP)” architecture, developed so that robots could easily cooperate based on a group of predefined plays, called the playbook. The development of the new strategy algorithm presented in this paper, used by the RoboCup Middle Size League LAR@MSL team, had a completely different approach from most other teams for multiple reasons. Contrary to the typical STP architecture, this strategy, called the Probability-Based Strategy (PBS), uses only skills and decides the outcome of the tactics and plays in real-time based on the probability of arbitrary values given to the possible actions in each situation. The action probability values also affect the robot’s positioning in a way that optimizes the overall probability of scoring a goal. It uses a centralized decision-making strategy rather than the robot’s self-control. The robot is still fully autonomous in the skills assigned to it and uses a communication system with the main computer to synchronize all robots. Also, calibration or any strategy improvements are independent of the robots themselves. The robots’ performance affects the results but does not interfere with the strategy outcome. Moreover, the strategy outcome depends primarily on the opponent team and the probability calibration for each action. The strategy presented has been fully implemented on the team and tested in multiple scenarios, such as simulators, a controlled environment, against humans in a simulator, and in the RoboCup competition.

Small Wheel Design and Analysis of Omni Wheels in the RoboCup Middle Size League

Small Wheel Design and Analysis of Omni Wheels in the RoboCup Middle Size League

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

ロボカップ中型リーグのお話(アイ・サイ問答教室)

ロボカップ中型リーグのお話(アイ・サイ問答教室)

The design of an intelligent soccer-playing robot

Purpose – The purpose of this paper is to design intelligent robots operating in such dynamic environments like the RoboCup Middle-Size League (MSL). In the RoboCup MSL, two teams of five autonomous robots play on an 18- × 12-m field. Equipped with sensors and on-board computers, each robot should be able to perceive the environment, make decision and control itself to play the soccer game autonomously. Design/methodology/approach – This paper presents the design of our soccer robots, participating in RoboCup MSL. The mechanical platform, electrical architecture and software framework are discussed separately. The mechanical platform is designed modularly, so easy maintainability is achieved; the electronic architecture is built on industrial standards using PC-based control technique, which results in high robustness and reliability during the intensive and fierce MSL games; the software is developed upon the open-source Robot Operating System (ROS); thus, the advantages of ROS such as modularity, portability and expansibility are inherited. Findings – Based on this paper and the open-source hardware and software, the MSL robots can be re-developed easily to participate in the RoboCup MSL. The robots can also be used in other research and education fields, especially for multi-robot systems and distributed artificial intelligence. Furthermore, the main designing ideas proposed in the paper, i.e. using a modular mechanical structure, an industrial electronic system and ROS-based software, provide a common solution for designing general intelligent robots. Originality/value – The methodology of the intelligent robot design for highly competitive and dynamic RoboCup MSL environments is proposed.

Neural Network in Computer Vision for RoboCup Middle Size League

Robot World Cup Initiative (RoboCup) is a worldwide competition proposed to advance research in robotics and artificial intelligence. It has a league called RoboCup soccer devoted for soccer robots, which is a challenge because robots are mobile, fully autonomous, multi-agents, and they play on a dynamic environment. Moreover, robots must recognize the game entities, which is a crucial task during a game. A camera is usually used as an input system to recognize ball, opponents, soccer field, and so on. These elements may be recognized applying some tools of computational intelligence, for example an artificial neural network. This paper describes the application of an artificial neural network on middle size robotic football league, where a multilayer perceptron neural network is trained with the backpropagation algorithm, to classify elements on the image. Each output neuron represents an entity and its output value depends on the current entity that is present on the image. The results show that an artificial neural network successfully classified the entities. They were recognized even when similar color entities were present on the image.

1P1-S06 RoboCup中型リーグに向けた次世代サッカーロボットの開発(ロボカップ・ロボットコンテスト)

1P1-S06 RoboCup中型リーグに向けた次世代サッカーロボットの開発(ロボカップ・ロボットコンテスト)

Robust and real-time self-localization based on omnidirectional vision for soccer robots

Self-localization is the basis to realize autonomous ability such as motion planning and decision-making for mobile robots, and omnidirectional vision is one of the most important sensors for RoboCup Middle Size League (MSL) soccer robots. According to the characteristic that RoboCup competition is highly dynamic and the deficiency of the current self-localization methods, a robust and real-time self-localization algorithm based on omnidirectional vision is proposed for MSL soccer robots. Monte Carlo localization and matching optimization localization, two most popular approaches used in MSL, are combined in our algorithm. The advantages of these two approaches are maintained, while the disadvantages are avoided. A camera parameters auto-adjusting method based on image entropy is also integrated to adapt the output of omnidirectional vision to dynamic lighting conditions. The experimental results show that global localization can be realized effectively while highly accurate localization is achieved in real-time, and robot self-localization is robust to the highly dynamic environment with occlusions and changing lighting conditions.

A Survey on Visual Perception for RoboCup MSL Soccer Robots

Visual perception is the most important method for providing information about the competition environment for RoboCup Middle Size League (MSL) soccer robots. The paper reviews the advancement of visual perception in RoboCup MSL soccer robots from several points of view including the design and calibration of the vision system, the visual object recognition, the estimation of the object's motion, robot visual self-localization and multi-robot cooperative sensing. The research progress we have achieved is also introduced in this review. The developing trends and the future research focuses on this problem are also discussed.

Distributed Active Traction Control System Applied to the RoboCup Middle Size League

This work addresses the problem of traction control in mobile wheeled robots in the particular case of the RoboCup Middle Size League (MSL). The slip control problem is formulated using simple friction models for ISePorto Team Robots with a differential wheel configuration. Traction was also characterized experimentally in the MSL scenario for relevant game events. This work proposes a hierarchical traction control architecture which relies on local slip detection and control at each wheel, with relevant information being relayed to a higher level responsible for global robot motion control. A dedicated one axis control embedded hardware subsystem allowing complex local control, high frequency current sensing and odometric information procession was developed. This local axis control board is integrated in a distributed system using CAN bus communications. The slipping observer was implemented in the axis control hardware nodes integrated in the ISePorto Robots and was used to control and detect loss of traction. An external vision system was used to perform a qualitative analysis of the slip detection and observer performance results are presented.

2A2-Q10 自律型全方位移動サッカーロボットプラットホームの開発 : 第一報 : 駆動部の設計(ロボカップ・ロボットコンテスト)

Industrial society, robot arm has been working for painting and moldering since 1980's. Also, robot are used for boring work and so on. However, recently, many robots are used for entertainment and so on. RoboCup is one of the most active competition in the world. In this research, we aim to participate in the RoboCup Middle Size League competition. In this paper, we report about how to develop out hardware module and systems.

Robot Self-localization with Optimized Error Minimizing for Soccer Contest

This paper proposed an integrated solution for robot self-localization in the RoboCup middle size league. Firstly, a kind of radial scan-lines are introduced to preprocess the images from the omni-directional camera so as to reduce the computational load and improve efficiency. The key points of this algorithm are minimizing the errors between the mark points and the detect points. In order to improve the self-location performance and reduce the effect by the dithering image, an optimized approach was presented and the extended Kalman filter was utilized to fuse visual information, odometry information and digital compass data. Secondly, before applying this approach into our robots, we built the simulate environment and compared the result to the existing particle filtering approach with 200 particles. Simulation result showed this approach is computational efficient and precise. Our method is computationally cheaper than the particle filtering approach. Finally, we tested the approach in the real RoboCup middle size league robot contest field. The experiments showed the optimized error minimizing self-localization provided high level capabilities.

2A2-F07 ロボカップサッカーにおけるトラップ機能の開発(ロボカップ・ロボットコンテスト)

2A2-F07 ロボカップサッカーにおけるトラップ機能の開発(ロボカップ・ロボットコンテスト)

A modelling language for cooperative plans in highly dynamic domains

Cooperative behaviour is one of the challenges most pronounced in the RoboCup Middle Size League. Especially the dynamic nature of the domain, which calls for swift adaptation by each robot and the team as a whole, is a distinctive property of the league. The ability to establish highly responsive teamwork while facing unreliable communication and sensory noise is a key to successful soccer teams. Moreover, modelling such responsive, cooperative behaviour is difficult. In this work, we specify a novel model for cooperative behaviour geared towards highly dynamic domains, focussing on the language syntax and semantics. In our approach, agents estimate each other’s decision and correct these estimations once they receive contradictory information. We provide a comprehensive approach for agent teamwork featuring intuitive modelling capabilities for multi-agent activities, abstractions over activities and agents, and a clear operational semantics. Moreover, we briefly present a graphical modeling tool for cooperative strategies, which is based directly on the theory laid out, together with a practical framework for executing said strategies. We show experimentally the responsiveness and coherence of the resulting team play.

Ball velocity estimation method based on RANSAC and Kalman filter for soccer robots

A new method was proposed to estimate the ball velocity in the Robot World Cup ( RoboCup) Middle Size League ( MSL) more effectively. The method was based on Kalman filter and RANSAC algorithm. Firstly several frames of the ball positions were stored and then Kalman filter was used to optimize the positions. Hundreds of models were built and RANSAC algorithm was employed to calculate the best model as the final ball velocity. The experimental results show that the proposed algorithm is effective. Furthermore, RANSAC algorithm can eliminate the outlier effectively, so when there are lots of noises in the ball information, the ball velocity can still be estimated with high accuracy by using the presented algorithm, and higher robustness can be achieved being compared with the other existing methods.

A Robust Self-Localization Method Based on Omnidirectional Vision for Soccer Robots

According to the high dynamics of robot soccer competition in RoboCup Middle Size League and the deficiency of the current self-localization methods,a robust self-localization method based on omnidirectional vision is proposed.The method combines the particle filter localization with the matching optimization localization,and a camera parameter auto-adjusting algorithm based on image entropy is also proposed to adapt the output of omnidirectional vision to the dynamic lighting conditions.The experimental results show that global localization can be realized effectively with the proposed method while highly accurate self-localization is achieved in real-time,and robot’s self-localization is robust to the highly dynamic environment with occlusion and varying lighting conditions.

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.

Robot team coordination using dynamic role and positioning assignment and role based setplays

The coordination methodologies of CAMBADA, a robotic soccer team designed to participate in the RoboCup Middle-Size League (MSL), are presented in this paper. The approach, which relies on information sharing and integration within the team, is based on formations, flexible positionings and dynamic role and positioning assignment. Role assignment is carried out locally on each robot to increase its reactivity. Positioning assignment is carried out at a lower frequency by a coach agent following a new priority-based algorithm that maintains a competitive formation, covering the most important positionings when malfunctions lead to a reduction of the team size. Coordinated procedures for passing and setplays have also been implemented. With this design, CAMBADA reached the 1st place in RoboCup’2008 and the 3rd place in RoboCup’2009. Competition results and performance measures computed from logs and videos of real competition games are presented and discussed.

3D-objecttracking with a mixed omnidirectional stereo camera system

This paper presents a novel technique of stereo vision based on the combination of an omnidirectional camera and a perspective camera. The technique combines the 360° field of view of the omnidirectional camera with the long field of view of a perspective camera. We describe the setup of such a camera system and how it can be used to achieve 3D-position estimates. Furthermore, we develop a maximum likelihood approach and a Bayesian approach that are able to fuse monocular and binocular observations of the same object to estimate its position and movement and show how this technique can be applied successfully in the RoboCup MiddleSizeLeague.