Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions

This paper addresses the design and implementation of complex Reinforcement Learning (RL) behaviors where multi-dimensional action spaces are involved, as well as the need to execute the behaviors in real-time using robotic platforms with limited computational resources and training times. For this purpose, we propose the use of decentralized RL, in combination with finite support basis functions as alternatives to Gaussian RBF, in order to alleviate the effects of the curse of dimensionality on the action and state spaces respectively, and to reduce the computation time. As testbed, a RL based controller for the in-walk kick in NAO robots, a challenging and critical problem for soccer robotics, is used. The reported experiments show empirically that our solution saves up to 99.94% of execution time and 98.82% of memory consumption during execution, without diminishing performance compared to classical approaches.Comment: Accepted in the RoboCup Symposium 2017. Final version will be published at Springe

Obstacle Avoidance Functions on Robot Mirosot in the Departement of Informatics of UPN “Veteran”

The robot is a machine that can perform physicalactivity repeatedly, either with human control or worksautomatically with the use of artificial intelligence. In the process,the robot can perform various kinds of sports, one of which is abranch of football. Robot football match organized by theFederation of International Robot-Soccer Association (FIRA)consists of several categories, one of which Micro Robot SoccerTournament (MiroSot). MiroSot is five to five games consisting of arobot measuring 7.5 cm x 7.5 cm x 7.5 cm were able to move andadapt to the environment without human intervention. CurrentlyInformatics UPN "Veteran" Yogyakarta began to develop MiroSotbut there are still some problems found that the movement of therobot is irregular, so that frequent collisions of the robot opponent.So it takes a function to avoid obstacles on the robot MiroSot.Capitalize knowledge of Obstacle Avoidance of the book "SoccerRobotics" [1], the function of avoiding obstacles using the potentialfield based navigation univector algorithm to determine the futurepath of the robot and dodge the functionality tailored to thecharacteristics of the robot MiroSot Information Engineering UPN"Veteran" Yogyakarta. The program is created usingprogramming language C ++ with Visual Studio 2008 IDE and sentto the robot from the main computer via radio frequency, the robotcan move properly using speed camera support above 50 framesper second as robot vision. Function to avoid obstacles on the robotdefender position MiroSot in the Departement Informatics of UPN"Veteran" Yogyakarta made this using the function position tomove towards the goal and using mathematical calculations todetermine the movement path avoiding obstacles based on potentialfield. In the development of this function can avoid obstacles in theform of a robot team, not only the robot opponent avoided. Whenthe moving speed of the robot was given control of the speeddepends on the distance of the destination position or positions arealso obstacles. The use of sensors gyroscrope expected to provide aneffective movement while avoiding obstacles. The success rate usinga gyroscope sensor to avoid obstacles on the position of defender of96% and the average time needed to reach the goal position at 5:33seconds so much faster