BAHIA RT - TEAM DESCRIPTION PAPER FOR 3D SIMULATION LEAGUE 2013

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1 BAHIA RT - TEAM DESCRIPTION PAPER FOR 3D SIMULATION LEAGUE 2013 Alan Santos, Danilo Lima, Diego Frias, Emmanuel Argollo, Lorena Pereira and Marco A. C. Simões Rua Silveira Martins, 2555, Cabula Computer Archicteture and Operating Systems Group (ACSO/UNEB) Salvador, Bahia, Brasil alandossantossoaress@gmail.com, danilo.ll7@hotmail.com, diegofriass@gmail.com, enmanueru3553@gmail.com, lorena.santpe@gmail.com, msimoes@uneb.br Abstract The competitiveness of a soccer team of humanoid robots depends on the movements optimization and cognitive level of robots. This paper describes the current stage of team BahiaRT with its new architecture and its works in in progress. We also describe the future work. Keywords Robot soccer, autonomous robotics, optimization Resumo A competitividade de um time de futebol de robôs humanóides depende da otimização motora e do nível cognitivo dos robôs. Este artigo descreve o momento atual da equipe BahiaRT com sua nova arquitetura e seus trabalhos em desenvolvimento. Descrevemos também os trabalhos futuros. Keywords Futebol de robôs,robótica autônoma, otimização 1 Introduction Team Bahia RT has been developed since 2008 with participations in RoboCup 2009 and After RoboCup 2010, the entire agent s architecture has been redesigned. This paper describes the second version of team Bahia RT with its new architecture and its current development state. We also describe the future works. Using this new version on its earliest stage the team won the runner-up in Latin American RoboCup Open 2010 and the Third Place in Latin American RoboCup Open In this last competition the team has reached a more mature stage. It was the second best defense in the competition and has showed the best goalkeeper and kick skills. Based on this performance presented, we believe that it is time to test again the team in the Brazilian competition. In the following sections we describe the agent architecture and the modules we have already developed such as world model, movement manager, and artificial intelligence. We also describe the work in progress and future work. 2 Architecture The team Bahia RT is based on a modularized agent architecture which has its complexity concentrated on two aspects: implementation of basic movements and implementing reasoning rules. Fig. 1 illustrates the agent architecture as described below. The Sensor and Actuator enable communication between server and agent.the PKS (Perception Kinematic State) stores information about the kinematic state of the agent, the ball and the Figure 1: Bahia RT agent architecture. other agents present in the game. This state is divided on visual information and sensory intelligence (SI). The visual information provides the exact location of all objects including the agent and possible evolution of current states. The SI generate greater information storage of the agent about itself. The WM (World Model) was built using the singleton standard that inherits from the parent class ObjectsMemory a dataset of perception of the world collected by the agent. It works based on two data types: i) the primitive data, where the information is used in the exact form it was received from the server; ii) calculated data resulting from calculations performed on primitive data providing greater semantic information to the agent. The WM not only saves the current values, but also contains a vector which is stored the state of the environment in a certain amount of past cycles, which characterizes the memory. Within the class ObjectsMemory exists another class, MapReliability where are saved the update

2 cycle of each object, which are used to obtain the reliability of these. TIC (Tactical Intelligence Center)decides what action the agent should perform according to its role in the game (defender, forward, goalkeeper) and environment information from the WM. HL-TIC (High Level TIC) analyses the information and decides what action to take. LL- TIC (Low Level TIC) defines the best way to perform the action decided by HL-TIC. It defines the movement sequence which best describes the action using time and precision as parameters. The BMM (Body Movements Manager) is responsible for execution of motion scripts(alves and Souza, 2012). Each movement in the sequence defined by LL-TIC has a corresponding motion script. Scripts are divided into blocks and blocks divided into poses. The HL-BMM (High Level BMM) receives a LL-TIC movement and turns it into a sequence of blocks(argollo and Souza, 2012). The LL-BMM (Low Level BMM) receives and executes this sequence of blocks turning it into a sequence of poses that matches the chosen movement. It may take one or more server cycles to execute one movement. This modular approach can be repeated internally enhancing the agent control level. For example, a strategy module can be created above the TIC to define high-level AI strategies. This architecture is very flexible and extensible. It can be very useful as a base for newbie teams to start their agent projects. The WM instance can be accessed anywhere in the code using the getinstance() method that returns a reference to the WM. The world model is composed of several attributes as playing time, agent state that indicates whether the agent is fallen, lifting, standing or walking.the figure 1 Fig. 2 illustrates the class diagram of the WM. The classes stores information according your name, example: The class Field stores the dimensions of the field and lines positions(santos et al., 2013). A WM with informations reliable and updated are essentials to the intelligence and of the agent. To reach a better decision-making the agente also need have of knowledge of informations ancient. And this justify the implementation of the memory in Bahia RT code, had like support the UvA Trilearn 2001 paper, where we seek a model of memory simple and effective. The UvA Trilearn 2001 model have memory within of WM, so they are a single entity. The visual informations depend of the agents perspective. There are two kinds objects: Fixed(flags, field lines) and Dynamic(ball, players), that have basics attributes like position and velocity. The objects not seen are updated according velocity that had in past cycle, however the reliability about these information decrease each cycle when the object is not seen. Our basic idea was make a vector of WM, storing the instances ancients. However to be able to store copies of WM, this standard singleton became affiliated class of MemoryObjects, a dataset of perception of the world collected by the agent. The struct proposed and implemented was this: a class ObjectMemory that do serve like WM intermediate. In it are all objects before contained in WM. There is also a class ReliabilityMap that stores the update cycle of each object necessary in the memory. Within ReliabilityMap exist a getreability() method used to calculate reliability through of the formula: Reliability = 1 - (NcycleCurrent - NcycleUpdate)/100 Within in WM exist a list of MemoryObjects, in this list are stored as the instances ancient of the WM. A each cycle of update of the WM a instance (informations group) is added in the memory, in the list. 3 The World Model (WM) and The Memory Figure 2: Class Diagram of the World Model. The WM is updated by the update methods that are called every cycle. The main methods are updategamestate(), updateball(), updateteammate() and updateopponent(). 4 Drop detection In order to the agent to take decisions, it needs to analyse the field, its teammates and its opponents. One of the most important things to know about

3 our own teammates and opponents is whether or not they are able to get the ball, or defend against a foe. To be able to do so, the agent must be standing up. So it s very important to our agent checking if another robot is fallen or not. To verify the status of other agents, the agent uses the angle between it s own head and the other agent s head, and the other agent s foot. With both these angles, it calculates the diference. That difference is shown to change according to the distance in the form of a curve, and to be variable whether the target agent was on foot or laid down. In game, the agent should be able to differ between each, to do so, it was added a exponential function returning a delimiter, which separated the standing results from the laid ones. The function works as described: Y = A * exp (- B * (X - C)P ) for P = 0.5, A = 11.72, B = , C = and X = distance between agent and target. With ths exponential working we were able to get a 98% accuracy when verifying whether the target agent is fallen os standing. 5 TIC: Tactical Intelligence Center In order to the agent decide what action to take during the game, it was created the TIC. It is responsible for taking decisions during the simulation by analysis of the current state of the robot and the game (Factum and Frias, 2012). 5.1 Decisions based on the player s role Once the simulation starts and the agent processing begins, it receives a static role according to its number. The possible roles are: Forward, Goalkeeper and Defender. Each role leads to a different line of reasoning when taking a decision. There is a generic reasoning shared by all players roles and specific reasoning for each role Generic reasoning It s the first part in the robot decision taking and is shared between all agents in the team. It defines the most basic priorities for any player. During this stage, the agent updates it s Kinematic State and verifies if it s fallen. In this case, it sets the action of standing up as it s first priority. If the robot is already standing, it checks the field to know its position and finally, sets up the movement of its head, which is managed differently from the other parts of its body Forward reasoning. Once the Generic reasoning is over, the forward players starts their own specific reasoning. The Forward basic decision is to kick the ball, aiming for the goal, whenever possible. The first step is to verify if it s possible to kick in that exact moment. In this case, this action is taken. Otherwise, the agent will check if it is one of the two players closest to the ball among the agents of its own team. If so, the agent decides to walk towards the ball. If it isn t one of the two players closest to the ball, it will move towards the end line, while the ball is in its own team possession, so when one of the two closest to the ball kick it, the agent will be in a position with high probability to reach the ball Defender reasoning. The defender has a predefined sector for its own positioning in the field. When any opponent enters into this sector with the ball, the defender moves towards the ball trying to take its possession. If it is successful, the agent kicks the ball to a forward teammate. Whenever no opponent goes into the defender s sector, the agent still on its strategic position Goalkeeper reasoning. The goalkeeper is responsible for defending the goal. Once the opponent is close enough to kick the ball into the goal, it prepares itself to defend, by positioning in the middle of the goal. Once the ball is at a certain distance, the goalkeeper will fall down in the direction the ball is coming in order to block it. 5.2 Positioning The positioning of the robot is divided into three different types, which are used differently depending on the request. They are: PositionBallGoal, RelativePositioning and DefendingPositioning PositionBallGoal This positioning is responsible for making the agent position itself behind the ball, and close to it, aiming the opponent goal with its front vector pointing towards the goal. This method is capable of position the robot this way from whichever position it is, dodging the ball so it won t move it unnecessarily as it can be seen in the scenarios in Fig. 3. In the first scenario, the ball is ahead of the agent, so it ll just have to go to the point behind the ball and in the direction of Ball-Goal vector. In the second case, the agent has passed the spot where the ball is, therefore it must turn back, and position itself in the same vector as in the first case. The third case is the most complex, the agent is ahead of the ball and if it tries to go to the expected position simply like the other ways, it ll

4 model based on the player s role and find and reach a target, however check if the target is accessible and if the agent owner of the target is better agente to make the interceptation. Its basic structure consists on: DefineAction; SelectTarget; CalcTarget; GoToTarget and DelTarget DefineAction Defines one of five possible actions, from the state and position of the robot and the game state, returns action = [get up, fall, kick, none, walk] SelectTarget Chooses according to a priority table one reliable target. Returns target which can be the ball, a fixed point in the field, one opponent, if not able to intercept returns null CalcTarget Figure 3: PositionBallGoal scenarios. collide with the ball. So, to avoid that, the robot will first go to an auxiliary position, behind the ball, passing by its side, on a determined security distance, then, it will go to the target RelativePositioning Is the simplest method for moving the robot. It will move the agent towards any defined point in the field, using the coordinates received, with no concerns on opponents or teammates in the way DefendingPositioning Very similar to the PositionBallGoal, the DefendingPositioning will calculate the path the robot shall take to position itself in front of the opponent agent, between it and the agents own goal. 6 Work in progress Many improvements are being developed in our team to increase their competitiveness in general. The main improvements are: i) Remodel tactical intelligence center; ii) enhancing walking movement; iii) use agents communication to cooperation and coordination; iv) automated enviroment to tests. These improvements are detailed in following subsections. 6.1 Remodel the tactical intelligence center To increase the competitiveness of the team through the reasoning we are implementing a improvement in tactical intelligence center. Is a Calculates the point where the agent could intercept the target specified in the entry, this is fixed or mobile returns point. If not for the intercept returns null GoToTarget Send the actions necessary to reach a target to GMM DelTarget Delete the target if not accessible or reliable. 6.2 Enhancing walking movement Studies are underway to build a module for dynamic walking and optimize the static walking motion. To create the module are being adopted dynamic stability criteria as center mass. From the center of mass can be calculated if the agent is unstable, with the possibility of falling and perform the necessary calculation for to reposition of the feet with respect to the trunk and stabilize the movement of the agent. The optimization of the static motion of the Bahia RT is being accomplished with the use of a Trainer, a tool developed to test the agent Bahia RT to generate optimal values. 6.3 Cooperation and coordination through communication The introduce of the agents communication was completed where the goalkeeper always send the ball s position to the other teammates. This way, every agent in our team always know the location of the ball and can decide to walk to it when necessary. Coordination can be used of various ways, in which the agents needs to share resources for

5 the execution of their activities, or when a possible group of activities contributes to the solution of bigger problems, or when there are several ways to achieve a goal(frozza, 2004) which is where the context of our study will be based. The evolved communication makes possible coordination of moves and positions predefined. 6.4 Automated enviroment to tests Studies are being conducted to develop an automated environment for testing mechanisms of locomotion autonomous agent that controls a humanoid robot with the aim of collecting performance indicators of the simulated 3D agents. Factum, R. and Frias, D. (2012). Proposal of architecture for a non-cooperative and nonconcurrent self adaptive multi agent system on reactive level for a robot soccer team, in UNEB (ed.), XVI Journey of Scientific Initiation of the UNEB, UNEB. (in Portuguese). Frozza, R. (2004). Standard to estimate Multi- Agent Coordination/ por Rejane Frozza, PhD thesis, Universidade Federal do Rio Grande do Sul. Santos, A., Cruz, A., Frias, D., Argollo, E., Souza, J., Pereira, L., Simoes, M. and Alves, M. (2013). Bahia3d - a team of 3d simulation for robocup, Robocup Conclusions Team Bahia RT has been developed with the objective to serve as a framework for testing experiments in the areas of research of group Bahia Robotics Team. Our main research areas are optimization, multiagent systems, planning and machine learning. The current agent architecture described in this paper supports our research interests. The new World Model is more flexible and represents an important part of the agents knowledge base. The new movements manager structure leads to a very stable movement. The unique weakness is the current movement low velocity for some important movements like walking. Using our research results in optimization, we plan to enhance our walking movement enabling a better team performance. We will use two approaches as describe in this paper. On a first approach offline optimization will be used to enhance the current motion scripts. In the second approach we will use real-time optimization to fine tune the movements during agent execution. We are also improving communication in the team to enable the use of coordination as an advanced cognitive feature for the agent structure. Considering the current stage and the work in progress there is a high chance to present a very competitive team in CBR References Alves, M. R. R. and Souza, J. R. d. (2012). Implementation manager at body movements in two levels agent team bahia3d soccer robots, in UNEB (ed.), XVI Journey of Scientific Initiation of the UNEB, UNEB. (in Portuguese). Argollo, E. M. and Souza, J. R. d. (2012). Restructuring of the motor intelligence of agent team football robots bahia3d., in UNEB (ed.), XVI Journey of Scientific Initiation of the UNEB, UNEB. (in Portuguese).

LegenDary 2012 Soccer 2D Simulation Team Description Paper

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