1,367 research outputs found
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
Communications for cooperation: the RoboCup 4-legged passing challenge
Communications are the basis for the collaborative
activities in the TeamChaos 4-legged team. In this paper we
present the communications architecture developed both to let
teammates communicate, and to easy the debugging of robot
behaviors from external computers. Details of its implementation
on the aiBo robots are also given. Using this infrastructure we
describe a protocol for role exchange named Switch! that we have
created. We also describe the use of both the communication
architecture, and the Switch! protocol in the passing challenge
of the 2006 edition of the RoboCu
FC Portugal 3D Simulation Team: Team Description Paper 2020
The FC Portugal 3D team is developed upon the structure of our previous
Simulation league 2D/3D teams and our standard platform league team. Our
research concerning the robot low-level skills is focused on developing
behaviors that may be applied on real robots with minimal adaptation using
model-based approaches. Our research on high-level soccer coordination
methodologies and team playing is mainly focused on the adaptation of
previously developed methodologies from our 2D soccer teams to the 3D humanoid
environment and on creating new coordination methodologies based on the
previously developed ones. The research-oriented development of our team has
been pushing it to be one of the most competitive over the years (World
champion in 2000 and Coach Champion in 2002, European champion in 2000 and
2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation
and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and
European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes
some of the main innovations of our 3D simulation league team during the last
years. A new generic framework for reinforcement learning tasks has also been
developed. The current research is focused on improving the above-mentioned
framework by developing new learning algorithms to optimize low-level skills,
such as running and sprinting. We are also trying to increase student contact
by providing reinforcement learning assignments to be completed using our new
framework, which exposes a simple interface without sharing low-level
implementation details
Real-time Coordinate Estimation for Self-Localization of the Humanoid Robot Soccer BarelangFC
In implementation, of the humanoid robot soccer consists of more than three robots when played soccer on the field. All the robots needed to be played the soccer as human done such as seeking, chasing, dribbling and kicking the ball. To do all of these commands, it is required a real-time localization system so that each robot will understand not only the robot position itself but also the other robots and even the object on the field’s environment. However, in real-time implementation and due to the limited ability of the robot computation, it is necessary to determine a method which has fast computation and able to save much memory. Therefore, in this paper we presented a real-time localization implementation method using the odometry and Monte Carlo Localization (MCL) method. In order to verify the performance of this method, some experiment has been carried out in real-time application. From the experimental result, the proposed method able to estimate the coordinate of each robot position in X and Y position on the field.Dalam implementasinya, robot humanoid soccer terdiri lebih dari tiga robot di lapangan ketika sedang bermain bola. Semua robot diharapkan dapat memainkan sepak bola seperti manusia seperti mencari, mengejar, menggiring bola dan menendang bola. Untuk melakukan semua perintah tersebut, diperlukan sistem lokalisasi real-time sehingga setiap robot tidak hanya memahami posisi robotnya sendiri tetapi juga robot-robot lain bahkan objek yang berada di sekitar lapangan. Namun dalam implementasi real-time dan karena keterbatasan kemampuan komputasi robot, diperlukan suatu metode komputasi yang cepat dan mampu menghemat banyak memori. Oleh karena itu, dalam makalah ini menyajikan metode implementasi lokalisasi real-time dengan menggunakan metode odometry and Monte Carlo Localization (MCL). Untuk memverifikasi kinerja metode ini, beberapa percobaan telah dilakukan dalam aplikasi real-time. Dari hasil percobaan, metode yang diusulkan mampu mengestimasi koordinat posisi robot pada posisi X dan Y di lapangan ketika sedang bermain bola
High level coordination and decision making of a simulated robotic soccer team
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
State and prospects of development of team interaction of robots on the example of competitions of the world tournament "Robocup"
Today, effective group work management is one of the main problems of mechatronics. As the development of generalized algorithms and principles of management is at an early level, the scientific community has formed several model tasks, one of which reads as follows: "By the middle of the XXI century the winner of the last world championship”. As part of the wording, the world's first RoboCup competition was launched in 1996 to promote research in the field of robot design and artificial intelligence.
The main task of the article is to analyze and highlight the current state of algorithms for command control of robots on the example of the RoboCup world tournament. The article describes the general schemes of team interaction in the divisions of the tournament, the hardware characteristics of the agents, the history, chronological development and the current state of the rules of the divisions. Based on the analysis, a comparative table of basic technical parameters of RoboCup leagues and approaches used for team management is formed. The conclusion concerning the most actual directions of researches of methods of group interaction is made
Petri Net Plans A framework for collaboration and coordination in multi-robot systems
Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling multi-robot systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration
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