Robot Soccer Research Articles

Applying hybrid learning approach to RoboCup's strategy

RoboCup (Robot world cup tournament) soccer game is a competitive game that has become a popular research domain in recent years since it involves a complex system for the behavior of multiple agents. In this paper, a hybrid approach, case-based reasoning genetic algorithm (CBR-GA) is applied to the soccer game for providing better strategies. By using CBR-GA, the soccer robots can obtain the suitable strategies for different conditions and store the related experiences, which may be reused in the future. Rule-based reasoning (RBR) will be employed to create a new strategy for the soccer robots when CBR-GA cannot provide a suitable one. A multi-agent learning system, constructed by combining case-based reasoning genetic algorithm with RBR strategy (CGRS), is implemented on the latest WrightEagle simulation platform that is released in 2011. In the CGRS system, two kinds of agent, namely “coach agent” and “movement agent”, are designed for the soccer game. The coach agent is responsible for deciding on the strategy goal and assigning tasks to the movement agents. Every movement agent will then execute its respective task for achieving the strategy goal. Better basic skills will facilitate the movement agents to execute more effectively the assigned tasks or plans; hence, many basic skills are designed for training the movement agents. To increase learning efficiency, the strategy cycle time is reduced with a suitable case base. To validate the effectiveness of the proposed approach, our soccer team played with the WrightEagle soccer team which has remained in the top two positions in simulation 2d in recent years. Our team gradually gets higher winning frequency in 50 rounds. Furthermore, a comparison experiment shows that the proposed approach has higher winning frequency than other methods including CBR-GA, CBR-RBR and RBR. Finally, the proposed approach is also found to have better learning mechanisms than other learning approaches in soccer game.

Automatic code generation of SIMUROSOT game strategies

Robot soccer is a multidisciplinary research field of growing academic interest. Scientific competitions worldwide have driven major technological advances in the field, since research institutes and universities often enroll their robotic teams and present the developments achieved during the previous year. The FIRA is a leading international organization that promotes such type of competition. One of its various competition categories is that of virtual robots competing in a simulation platform called SIMUROSOT. This 3D simulation platform is available so that researchers and students can develop robot teams and test their performance appropriately and repeatedly. The major problem with the mainstream encoding mechanism used in the preparation of robot teams for SimuroSot is the lack of a formal process to standardize the programming and model the game strategies. Such lack of standardization causes several limitations on the competition as a science promoter, namely: (1) the difficulty of understanding other's code, (2) absence of abstraction for the conceiving of game strategies, (3) need for high programming skills in order to produce effective game strategies and (4) lack of clear separation between robots movimentation primitives and specific application domain concerns - in this case, the soccer domain. This paper proposes a semi-formal specification for the modeling of team game strategies to the FIRA SIMUROSOT category. This specification defines two levels of mapping: (1) mapping of the application domain information - in this case, the soccer competition - to Finite State Machines and (2) mapping of Finite State Machines that represent game strategies of a team to their respective coding in a target programming language, making use of coding standards that ensure the readability and maintainability of the final code. Encodings of game strategies in the programming language Lingo has been provided following the proposal. The error-free execution of such encodings on the simulation platform shows the proposal feasibility.

Colored Ball Position Tracking Method for Goalkeeper Humanoid Robot Soccer

The goalkeeper robot in a robot soccer game must be able to detect position of the ball and block the ball into the goal. Identifying the location of the ball using robot vision becomes very important in addition to the mechanical motion of the robot. This paper proposed method to track colored ball position for goalkeeper robot. First, image is captured with web camera and then color filtering based on HSL color model performed on each image frame in the video. After that, images from filtering process are detected its blob by labeling nearest connected components. The largest blob in image represents the ball. The next step is detection of ball position using 9-Cells coordinate. Ball position coordinate is updated in some period of time and then sends to robot controller to conduct the robot's movements appropriately. Experiment results show that system is able to detect the ball and its position in 9-Cells coordinate which can be used by goalkeeper robot soccer controller to block the ball.

Colored Ball Position Tracking Method For Goalkeeper Humanoid Robot Soccer

Indonesian, as one of the countries with high number of internet users has the potential to serve as the place with great information resources. However, these resources must be accompanied by the availability of dependable information. Information trustworthiness can be obtained by assessing the confidence level (trust) of the source of information. This can be determined by using trust scope attributes. Hence, in this study, we intended to establish the trust scope attributes by means of utilizing the ones contained in the User Profile provided by social media; in this case Facebook, Google+, Twitter, and Linkedin. We carried out the research by conducting four stages namely data collection, attributes grouping, attribute selection, and surveys. A survey was then distributed to 257 randomly selected respondents (divided into two clusters: civilians and military officers) to seek for their opinions in terms of what attributes were considered to be crucial in defining the believability of an information source. Chi-square Goodness of fit Test was conducted to compare observed data with data we would expect to obtain. The results of the research suggested that there was similar judgment in terms of dictating source of information trustworthiness chosen by the research participants with the attributes provided by trust scope category. In this research, both civilians and military officer clusters concurrently perceived that educational background was the most dependable attribute.

Vision in robot soccer: a review

This paper will give readers an overview of the vision system used on Robot Soccer systems. Firstly, it lists out the positioning of the cameras that are used on a robot soccer system both for FIRA and RoboCup. Here various position of camera placement is explained; among them are the global and local visions. This is further broken down to center and side positioning for global vision. For local vision, it is divided into three, namely omni-directional, binocular/stereo and monocular. Next, image processing algorithms will be explained and related reference with their advantages and disadvantages. The reviewed algorithms are Kalman Filter, CamShift and Optical Flow. Brief description of each algorithm is also included.

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.

Realization of Robot-assisted Laser Scanning Measurement System based on Assembly Platform

The assembly platform of complex structures with a variety of CNC tools can achieve automated clamping, riveting, drilling, seamless calibration docking, and can also realize connecting engineering of complex assembly. In this paper, to solve the issue of digital measurement positioning technology in complex-structure assembly platform, an industrial robot-assisted laser scanning system is designed, and its kinematics model is established. At the same time, the trajectory of the robot rotation scan is planned. Finally, the measurement accuracy of the system we have established proves to meet the ac- tual demands by assembling experiment. In the assembly of complex-structure, digitized measurement positioning technology is an important standard of assembly quality. The technology can be realized by the robots equipped with vision meas- urement system, which is real-time scanning, high measuring accuracy and efficiency. In the 1960s, there were researches of combining visual measurement systems and robot in the world. In 1969, Nilsson developed the first intelligent mobile robot named shakey equipped with a camera, rangefinder, and contact sensors at Stanford SRI Institute, and its task was automatic obstacle avoidance and target motion tracking. In the late 1990s, machine vision measurement technology be- gan to develop rapidly in China. Harbin Institute of Technology achieved a fully autonomous soccer robot navigation with heterogeneous binocular active vision systems, analyzed fusion method of foot- ball robot's navigation data based on the binocular coordination, and proposed the binocular coordina- tion navigation model and target implementation strategy based on its behavior (1). National University of Defense Technology studied surgical instrument tracking and positioning technology based on bin- ocular vision (2). Currently, machine vision technology is widely used in various aspects, especially in industrial production and assembly. The machine vision measurement technology has become increas- ingly mature, but the study of improving the measurement accuracy of vision measurement system is still a main direction that we need to further study. In this paper, through analyzing the complex-structure assembly and detection system, a laser scan- ning measurement system is built by the Swedish ABB robot equipped with a CCD(Charge-coupled Device) high-speed camera. The system proves to be large measurement range, high accuracy and real- time.

Multiagent Reinforcement Learning with Regret Matching for Robot Soccer

This paper proposes a novel multiagent reinforcement learning (MARL) algorithm Nash- learning with regret matching, in which regret matching is used to speed up the well-known MARL algorithm Nash- learning. It is critical that choosing a suitable strategy for action selection to harmonize the relation between exploration and exploitation to enhance the ability of online learning for Nash- learning. In Markov Game the joint action of agents adopting regret matching algorithm can converge to a group of points of no-regret that can be viewed as coarse correlated equilibrium which includes Nash equilibrium in essence. It is can be inferred that regret matching can guide exploration of the state-action space so that the rate of convergence of Nash- learning algorithm can be increased. Simulation results on robot soccer validate that compared to original Nash- learning algorithm, the use of regret matching during the learning phase of Nash- learning has excellent ability of online learning and results in significant performance in terms of scores, average reward and policy convergence.

Design and Implementation of an Autonomous Robot Soccer System

Abstract A design and implementation method of a robot soccer system with three vision-based autonomous robots is proposed in this paper. A hierarchical architecture with four independent layers: (a) information layer, (b) strategy layer, (c) tactics layer, and (d) execution layer, is proposed to construct a flexible and robust vision-based autonomous robot soccer system efficiently. Five mechanisms, including (a) a two-dimensional neck mechanism, (b) dribbling mechanism, (c) shooting mechanism, (d) aiming mechanism, and (e) flexible movement mechanism, are proposed to mean the robot with multiple functions can win the game. A method based on data obtained from a compass and a vision sensor is proposed to determine the location of the robot on the field. In the strategy design, a hierarchical architecture of decision based on the finite-state transition mechanism for the field players and the goalkeeper is proposed to solve varied situations in the robot soccer game. Three vision-based robots are implemented and some real competition results in the FIRA Cup are presented to illustrate the validity and feasibility of the proposed method in autonomous robot soccer system design.

Optimization of Goal Targeting in 1 on 1 Soccer Robots Using Safe Points (Experimental Research)

Due to the fact that targeting the goal is the last part of soccer robot’s aggressive decision chains, aiming styles and their lack of proficiency are discussed. Then targeting based on safe points is explained and implemented. Accordingly, this method has perfect accuracy. It brings new standpoint in aiming and it’s capable of moving the barrier of its related knowledge.

Research of Robocop Simulation Platform for Robot Soccer

With the development of computer technology, Multi-Agent System of Distributed Artificial Intelligent is advanced and implemented. This paper discusses the method for realizing the offences and defences of intelligent robot in Robocop simulate competition.

Towards Behavior Control for Evolutionary Robot Based on RL with ENN

This paper proposes a behavior-switching control strategy of an evolutionary robot based on Artificial Neural Network (ANN) and genetic algorithm (GA). This method is able not only to construct the reinforcement learning models for autonomous robots and evolutionary robot modules that control behaviors and reinforcement learning environments, and but also to perform the behavior-switching control and obstacle avoidance of an evolutionary robot in the unpredictable environments with the static and moving obstacles by combining ANN and GA. The experimental results have demonstrated that our method can perform the decision-making strategy and parameter setup optimization of ANN and GA by learning and can effectively escape from a trap of local minima, avoid motion deadlock status of humanoid soccer robotic agents, and reduce the oscillation of the planned trajectory among the multiple obstacles by crossover and mutation. We have successfully applied some results of the proposed algorithm to our simulation humanoid robotic soccer team CIT3D which won the 1st prize of RoboCup Championship and ChinaOpen2010 and the 2nd place of the official RoboCup World Championship on 5-11, July 2011 in Istanbul, Turkey. In comparison with the conventional behavior network and the adaptive behavior method, our algorithm simplified the genetic encoding complexity, improved the convergence rate ρ and the network performance.

Reaching High Interactive Levels with Situated Agents

The paper presents a coordinated process through which the situated agents are capable of working jointly using knowledge about their skills to interact with other similar entities. A good coordination is reached because each situated agent is able to represent its suitability rates to perform any action in an effective way. Besides using such rates, an agent can coordinate its actions with other agents. In this sense, each situated agent tries to select and perform only the actions with the highest execution rates. Some experiments in a real robotic soccer test-bed are designed to put in practice the proposed coordinated approach. Final remarks conclude the effectiveness and advantages of the presented work in order to increase the performance of a team composed by situated agents when they must solve complex tasks in a dynamical, competitive and unpredictable scenario.

A Novel Generic Ball Recognition Algorithm Based on Omnidirectional Vision for Soccer Robots

It is significant for the final goal of RoboCup to realize the recognition of generic balls for soccer robots. In this paper, a novel generic ball recognition algorithm based on omnidirectional vision is proposed by combining the modified Haar-like features and AdaBoost learning algorithm. The algorithm is divided into offline training and online recognition. During the phase of offline training, numerous sub-images are acquired from various panoramic images, including generic balls, and then the modified Haar-like features are extracted from them and used as the input of the AdaBoost learning algorithm to obtain a classifier. During the phase of online recognition, and according to the imaging characteristics of our omnidirectional vision system, rectangular windows are defined to search for the generic ball along the rotary and radial directions in the panoramic image, and the learned classifier is used to judge whether a ball is included in the window. After the ball has been recognized globally, ball tracking is realized by integrating a ball velocity estimation algorithm to reduce the computational cost. The experimental results show that good performance can be achieved using our algorithm, and that the generic ball can be recognized and tracked effectively.

Vision Based Self Localization for Humanoid Robot Soccer

Vision based robot soccer localization system, aiming to create a robot soccer localization system. The implementation of this idea is motivated by the need of the localization system for the robot soccer to play the ball effectively and efficiently. The expected result of this research is to develop the localization system for the robot soccer based on camera vision. Thus the developed robot will be able to play football better. It is because of the localization system that can make the robot able to behave more precisely on certain condition and location. For example, the robot should be able to find the ball in its own area or in the opponent’s. Research conducted on several major parts, consisting: image capture, processing image information and directions to the information about the location of the robot at the time. The experimental results indicate that the localization system in this research work has the ability to perform an approximation of the robot location with respect to the goal.

복소 포텐셜을 이용한 로봇 축구용 다개체 로봇의 경로 계획

This paper deals with the trajectory planning of multi agent robots using complex potential theory for robot soccer. The complex potential theory is introduced, then the circle theorem is used to avoid obstacles, and the vortex pair is used to make precise kicking direction of robot. Various situations of robot soccer are simulated and the effect of vortex strength and the speed of robots are discussed and the better way to avoid obstacles and to kick the precise direction is found. The feasibilities of complex potential theory to apply for the multi agent robots are successful.

Design and Implementation of Goalkeeper Defensive Strategy on the Soccer Robot Simulation Game

Robot soccer is a new research topic of artificial intelligence. The robot soccer game can be simply simulated by software. As the core part of defensive strategy, the goalkeeper's defensive is also a difficult subject in the soccer robot game. The site is devised by the rules, and the area of ball is as the center. This strategy does not consider the other player's position as well as their overall offensive state. It is not adopt to determine some special cases. A novel defensive strategy of goalkeeper is designed, and the experiments in different arithmetic are done and the results are noted. It needs to be further improved in guarantee real-time performance

Design and Implementation of Robot Soccer Communication Protocol Based on Ant Colony Algorithm

Robot soccer is an effective platform which is used to validate techniques such as artificial intelligence. Due to the limitations of a single robot, complex tasks on pitch often need to be completed through coordination of multiple robots. The purpose of this paper is to realize the information transfer between robots and thus to achieve information sharing by designing the network communication system among robots, as well as to provide forceful support for robots' decision-making and multi-robots cooperation. Meanwhile, the ant colony algorithm,which comes after the realization of robot communication system, is introduced to solve the problem existed in multiple-robots coordination. Experimental results show that the communication system in this paper can effectively fulfill information transfer between robots and has laid a good foundation for cooperation of robots.

Vision Based Self Localization for Humanoid Robot Soccer

Vision Based Self Localization for Humanoid Robot Soccer

An automatic approach to extract goal plans from soccer simulated matches

Soccer is a competitive and collective sport in which teammates try to combine the execution of basic actions (cooperative behavior) to lead their team to more advantageous situations. The ability to recognize, extract and reproduce such behaviors can prove useful to improve the performance of a team in future matches. This work describes a methodology for achieving just that makes use of a plan definition language to abstract the representation of relevant behaviors in order to promote their reuse. Experiments were conducted based on a set of game log files generated by the Soccer Server simulator which supports the RoboCup 2D simulated robotic soccer league. The effectiveness of the proposed approach was verified by focusing primarily on the analysis of behaviors which started from set-pieces and led to the scoring of goals while the ball possession was kept. One of the results obtained showed that a significant part of the total goals scored was based on this type of behaviors, demonstrating the potential of conducting this analysis. Other results allowed us to assess the complexity of these behaviors and infer meaningful guidelines to consider when defining plans from scratch. Some possible extensions to this work include assessing which plans have the ability to maximize the creation of goal opportunities by countering the opponent's team strategy and how the effectiveness of plans can be improved using optimization techniques.