Development of an Open Humanoid Robot Platform for Research and Autonomous Soccer Playing
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Robocup a Challenge Problem for Artificial Intelligence
RoboCup A Challenge Problem for Artificial Intelligence Michail G. Lagoudakis Intelligent Systems Laboratory Department of Electronic and Computer Engineering Technical University of Crete Chania, Greece Democritus Summer School 2010 July 2010, Athens, Greece RoboCup: A Challenge Problem for Artificial Intelligence Sunday 17 July 2050 Spain vs Robots Soccer City Stadium Summer School 2010 Michail G. Lagoudakis Page 2 RoboCup: A Challenge Problem for Artificial Intelligence Humans vs. Robots 2010 Summer School 2010 Michail G. Lagoudakis Page 3 RoboCup: A Challenge Problem for Artificial Intelligence Where do we stand today? RoboCup 2010 Humanoid KidSize League Final (Dribblers vs. Fumanoids) Summer School 2010 Michail G. Lagoudakis Page 4 RoboCup: A Challenge Problem for Artificial Intelligence Where do we stand today? RoboCup 2010 Humanoid TeenSize League Final (Nimbro vs. CIT Brains) Summer School 2010 Michail G. Lagoudakis Page 5 RoboCup: A Challenge Problem for Artificial Intelligence Talk Outline RoboCup The Aldebaran Nao Robot Standard Platform League Team Kouretes Kouretes Research Summer School 2010 Michail G. Lagoudakis Page 6 RoboCup Robot Champions! RoboCup: A Challenge Problem for Artificial Intelligence RoboCup RoboCup – international robotic soccer world cup – 1994: idea conceived by Hiroaki Kitano – today: RoboCup federation [ www.robocup.org ] Vision – “By the year 2050, to develop a team of fully autonomous humanoid robots that can win against the human world soccer champions ” – ambitious endeavor similar to sending a man to the moon – "One small step for a ROBOT, one giant leap for mankind." TM Extensions – RoboRescue: search and rescue missions – RoboCup Junior, RoboCup@home, RoboDance Summer School 2010 Michail G. Lagoudakis Page 8 RoboCup: A Challenge Problem for Artificial Intelligence RoboCup Divisions Summer School 2010 Michail G. -
Remote and Autonomous Controlled Robotic Car Based on Arduino with Real Time Obstacle Detection and Avoidance
Universal Journal of Engineering Science 7(1): 1-7, 2019 http://www.hrpub.org DOI: 10.13189/ujes.2019.070101 Remote and Autonomous Controlled Robotic Car based on Arduino with Real Time Obstacle Detection and Avoidance Esra Yılmaz, Sibel T. Özyer* Department of Computer Engineering, Çankaya University, Turkey Copyright©2019 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract In robotic car, real time obstacle detection electronic devices. The environment can be any physical and obstacle avoidance are significant issues. In this study, environment such as military areas, airports, factories, design and implementation of a robotic car have been hospitals, shopping malls, and electronic devices can be presented with regards to hardware, software and smartphones, robots, tablets, smart clocks. These devices communication environments with real time obstacle have a wide range of applications to control, protect, image detection and obstacle avoidance. Arduino platform, and identification in the industrial process. Today, there are android application and Bluetooth technology have been hundreds of types of sensors produced by the development used to implementation of the system. In this paper, robotic of technology such as heat, pressure, obstacle recognizer, car design and application with using sensor programming human detecting. Sensors were used for lighting purposes on a platform has been presented. This robotic device has in the past, but now they are used to make life easier. been developed with the interaction of Android-based Thanks to technology in the field of electronics, incredibly device. -
AUV Adaptive Sampling Methods: a Review
applied sciences Review AUV Adaptive Sampling Methods: A Review Jimin Hwang 1 , Neil Bose 2 and Shuangshuang Fan 3,* 1 Australian Maritime College, University of Tasmania, Launceston 7250, TAS, Australia; [email protected] 2 Department of Ocean and Naval Architectural Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada; [email protected] 3 School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China * Correspondence: [email protected] Received: 16 July 2019; Accepted: 29 July 2019; Published: 2 August 2019 Abstract: Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed. Keywords: autonomous underwater vehicle(s); maritime robotics; adaptive sampling; underwater feature tracking; in-situ sensors; sensor fusion 1. Introduction Autonomous underwater vehicles (AUVs) are unmanned marine robots. Owing to their mobility and increased ability to accommodate sensors, they have been used for a broad range of oceanographic missions, such as surveying underwater plumes and other phenomena, collecting bathymetric data and tracking oceanographic dynamic features. -
2018 RCJ Soccer Rules
RoboCupJunior Soccer - Rules 2018 RoboCupJunior Soccer Technical Committee RoboCupJunior General Chairs Marek Šuppa (Slovakia) CHAIR Irene Kipnis (Israel) CHAIR Felipe Nascimento (The Netherlands) Roberto Bonilla (Mexico) James Riley (Australia) Nerea de la Riva (Sweden) Javier Delgado (México) Michael Sloan Warren (USA) Trustees representing RoboCupJunior Sarit Salzman (Israel) Amy Eguchi (USA)* Fernando Ribeiro (Portugal) Gerard Elias (Australia) * RoboCup Federation Vice President representing RCJ Gerald Steinbauer (Austria) These are the official Soccer rules for RoboCupJunior 2018. They are released by the RoboCupJunior Soccer Technical Committee. The English version of these rules has priority over any translations. Items in red represent significant rules changes introduced this year. Teams are advised to check the RoboCupJunior Soccer site http://junior.robocup.org/soccer/ for OC (Organizational Committee) procedures and requirements for the international competition. Each team is responsible for verifying the latest version of the rules prior to competition. Preface In the RoboCupJunior soccer challenge, teams of young engineers design, build, and program two fully autonomous mobile robots to compete against another team in matches. The robots must detect a ball and score into a color-coded goal on a special field that resembles a human soccer field. Figure 1: Two teams of two robots with an orange ball on a RoboCupJunior Soccer field. Final Rules as of April 23rd, 2018 Page 1 of 40 To be successful, participants must demonstrate skill in programming, robotics, electronics and mechatronics. Teams are also expected to contribute to the advancement of the community as a whole by sharing their discoveries with other participants and by engaging in good sportsman- ship,regardless of culture, age or result in the competition. -
The French Robocup Team
RoboCup-99 Team Descriptions 0 NIL League, Team French-Team, pages 0–0 http://www.ep.liu.se/ea/cis/1999/NIL/2/ The French Robocup Team French-Team Kamel BOUCHEFRA, Vincent HUGEL, Patrick BONNIN, Pierre BLAZEVIC, Dominique DUHAUT DD: Paris 6 University PB: Versailles Saint Quentin En Yvelines University VH: Laboratoire de Paris KB, PB: Paris 13 University Abstract. This paper presents the software components designed by the LRP team and which are intended to make the Sony Pet Robots behave as powerful organized soccer players. These components comprise a locomo- tion module, a vision module and a strategy module. The article explains the personal background of the authors, this includes the experience from earlier projects. The key words of this work are the following : Pet Robots, auton- omy, perception, concurrency, strategy. The French RoboCup Team is composed of Dominique DUHAUT, Pierre BLAZEVIC, Patrick BONNIN, Vincent HUGEL and Kamel BOUCHEFRA. Pierre BLAZEVIC and Vincent HUGEL are both in charge of the robotics part of the project. They are the roboticians of our team. Pierre and Vincent have been involved in many competitions and exhibitions, among which the last RoboCup held in july 1998 in Paris. Pierre BLAZEVIC is an Associate Professor at Versailles Saint Quentin En Yvelines University. Vincent HUGEL is a PhD student at LRP (Laboratoire de Paris). Patrick BONNIN is in charge of the vision system. He has been involved in the last RoboCup held in july 1998 in Paris. Patrick BONNIN is an Associate Professor at Paris 13 university. Kamel BOUCHEFRA is a new member in the team, he is in charge of the strategy level within the subject. -
Learning Preference Models for Autonomous Mobile Robots in Complex Domains
Learning Preference Models for Autonomous Mobile Robots in Complex Domains David Silver CMU-RI-TR-10-41 December 2010 Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania, 15213 Thesis Committee: Tony Stentz, chair Drew Bagnell David Wettergreen Larry Matthies Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics Copyright c 2010. All rights reserved. Abstract Achieving robust and reliable autonomous operation even in complex unstruc- tured environments is a central goal of field robotics. As the environments and scenarios to which robots are applied have continued to grow in complexity, so has the challenge of properly defining preferences and tradeoffs between various actions and the terrains they result in traversing. These definitions and parame- ters encode the desired behavior of the robot; therefore their correctness is of the utmost importance. Current manual approaches to creating and adjusting these preference models and cost functions have proven to be incredibly tedious and time-consuming, while typically not producing optimal results except in the sim- plest of circumstances. This thesis presents the development and application of machine learning tech- niques that automate the construction and tuning of preference models within com- plex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback. -
Uncorrected Proof
JrnlID 10846_ ArtID 9025_Proof# 1 - 14/10/2005 Journal of Intelligent and Robotic Systems (2005) # Springer 2005 DOI: 10.1007/s10846-005-9025-1 Temporal Range Registration for Unmanned 1 j Ground and Aerial Vehicles 2 R. MADHAVAN*, T. HONG and E. MESSINA 3 Intelligent Systems Division, National Institute of Standards and Technology, Gaithersburg, MD 4 20899-8230, USA; e-mail: [email protected], {tsai.hong, elena.messina}@nist.gov 5 (Received: 24 March 2004; in final form: 2 September 2005) 6 Abstract. An iterative temporal registration algorithm is presented in this article for registering 8 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured 9 environments. We are primarily motivated by the development of 3D registration algorithms to 10 overcome both the unavailability and unreliability of Global Positioning System (GPS) within 11 required accuracy bounds for Unmanned Ground Vehicle (UGV) navigation. After suitable 12 modifications to the well-known Iterative Closest Point (ICP) algorithm, the modified algorithm is 13 shown to be robust to outliers and false matches during the registration of successive range images 14 obtained from a scanning LADAR rangefinder on the UGV. Towards registering LADAR images 15 from the UGV with those from an Unmanned Aerial Vehicle (UAV) that flies over the terrain 16 being traversed, we then propose a hybrid registration approach. In this approach to air to ground 17 registration to estimate and update the position of the UGV, we register range data from two 18 LADARs by combining a feature-based method with the aforementioned modified ICP algorithm. 19 Registration of range data guarantees an estimate of the vehicle’s position even when only one of 20 the vehicles has GPS information. -
Robocupjunior Rescue Line - Rules 2021 (Draft 01)
RoboCupJunior Rescue Line - Rules 2021 (Draft 01) RoboCupJunior Rescue Technical Committee 2020 Chair Kai Junge UK Naomi Chikuma Japan Tom Linnemann Germany Ryo Unemoto Japan Elizabeth Mabrey USA Tatiana Pazelli Brazil Alexis Cosette Arizaga Mexico RoboCupJunior General Chairs 2020 Trustees representing RoboCupJunior Chair Nerea de la Riva Iriepa Sweden Amy Eguchi USA Julia Maurer USA Fernando Ribeiro Portugal Shoko Niwa Japan Gerard Elias Australia Gerald Steinbauer Austria Official Resources RoboCupJunior Official Site RoboCupJunior Official Forum https://junior.robocup.org/ https://junior.forum.robocup.org/ Corrections and clarifications to the rules may be posted on the Forum in advance of updating this rule file. It is the responsibility of the teams to review the forum to have a complete vision of these rules. Last updated: 2020-07-14 Page 1 of 21 Before you read the rules Please read through the RoboCupJunior General Rules before proceeding on with these rules, as they are the premise for all rules. The English rules published by the RoboCupJunior Rescue Technical Committee are the only official rules for RoboCupJunior Rescue Line 2021. The translated versions that can be published by each regional committee are only reference information for non-English speakers to better understand the rules. It is the responsibility of the teams to have read and understood the official rules. Scenario The land is simply too dangerous for humans to reach the victims. Your team has been given a difficult task. The robot must be able to carry out a rescue mission in a fully autonomous mode with no human assistance. The robot must be durable and intelligent enough to navigate through treacherous terrain with hills, uneven land and rubble without getting stuck. -
Dynamic Reconfiguration of Mission Parameters in Underwater Human
Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration Md Jahidul Islam1, Marc Ho2, and Junaed Sattar3 Abstract— This paper presents a real-time programming and in the underwater domain, what would otherwise be straight- parameter reconfiguration method for autonomous underwater forward deployments in terrestrial settings often become ex- robots in human-robot collaborative tasks. Using a set of tremely complex undertakings for underwater robots, which intuitive and meaningful hand gestures, we develop a syntacti- cally simple framework that is computationally more efficient require close human supervision. Since Wi-Fi or radio (i.e., than a complex, grammar-based approach. In the proposed electromagnetic) communication is not available or severely framework, a convolutional neural network is trained to provide degraded underwater [7], such methods cannot be used accurate hand gesture recognition; subsequently, a finite-state to instruct an AUV to dynamically reconfigure command machine-based deterministic model performs efficient gesture- parameters. The current task thus needs to be interrupted, to-instruction mapping and further improves robustness of the interaction scheme. The key aspect of this framework is and the robot needs to be brought to the surface in order that it can be easily adopted by divers for communicating to reconfigure its parameters. This is inconvenient and often simple instructions to underwater robots without using artificial expensive in terms of time and physical resources. Therefore, tags such as fiducial markers or requiring memorization of a triggering parameter changes based on human input while the potentially complex set of language rules. Extensive experiments robot is underwater, without requiring a trip to the surface, are performed both on field-trial data and through simulation, which demonstrate the robustness, efficiency, and portability of is a simpler and more efficient alternative approach. -
Robots Are Becoming Autonomous
FORSCHUNGSAUSBLICK 02 RESEARCH OUTLOOK STEFAN SCHAAL MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS, TÜBINGEN Robots are becoming autonomous Towards the end of the 1990s Japan launched a globally or carry out complex manipulations are still in the research unique wave of funding of humanoid robots. The motivation stage. Research into humanoid robots and assistive robots is was clearly formulated: demographic trends with rising life being pursued around the world. Problems such as the com- expectancy and stagnating or declining population sizes in plexity of perception, effective control without endangering highly developed countries will create problems in the fore- the environment and a lack of learning aptitude and adapt- seeable future, in particular with regard to the care of the ability continue to confront researchers with daunting chal- elderly due to a relatively sharp fall in the number of younger lenges for the future. Thus, an understanding of autonomous people able to help the elderly cope with everyday activities. systems remains essentially a topic of basic research. Autonomous robots are a possible solution, and automotive companies like Honda and Toyota have invested billions in a AUTONOMOUS ROBOTICS: PERCEPTION–ACTION– potential new industrial sector. LEARNING Autonomous systems can be generally characterised as Today, some 15 years down the line, the challenge of “age- perception-action-learning systems. Such systems should ing societies” has not changed, and Europe and the US have be able to perform useful tasks for extended periods autono- also identified and singled out this very problem. But new mously, meaning without external assistance. In robotics, challenges have also emerged. Earthquakes and floods cause systems have to perform physical tasks and thus have to unimaginable damage in densely populated areas. -
Robocuppers! Attention! Your Technical Innovation Will Be Evaluated Prior to by Robocup Junior Rescue Organizing Committee the Start of the Field Competition
ROBOCUP JUNIOR RESCUE Robocup Junior Rescue July 11, 2014 WELCOME! NEWSLETTER #1 OPEN TECHNICAL EVALUATION Welcome RoboCuppers! Attention! Your technical innovation will be evaluated prior to by RoboCup Junior Rescue Organizing Committee the start of the field competition. We will focus on it the first days starting on the setup day (July 20th) at Once again, it is an honor for us to share, released by the RCJ Rescue Technical the evening on the working area. with the best teams around the world, the Committee and have priority over any experience of the RoboCupJunior world cup translation. Do not forget to bring your robot, poster, and the engineering journal printed out. Be prepared to championship. This time we will meet in the If you have any question please contact them explain about your work! beautiful city of João Pessoa, Brazil. How through the International RCJ Community quickly the time passes since we had the Forum at http://www.rcjcommunity.org/ opportunity to see each other in our 2013 edition at Eindhoven, the Netherlands and Inspection for others this will be a great first experience. As stated in the RoboCupJunior Rescue 2014 The Organizing Committee have worked rules under the 2.4 Inspection section, all the hard in the contest to be a great source of teams have to comply with: knowledge and experience for each one of “2.4.5 All teams will need to email a you. Here you will have the opportunity to technical document containing the major list test the hard work carried out in recent of hardware and software components […]” RCJ 2014 Members months. -
Rescue Simulation (Former Cospace) 2021 Rules – Final
RoboCupJunior Simulation (former CoSpace) – Rules 2021 RoboCupJunior Rescue Technical Committee 2020 RoboCupJunior General Chairs Kai Junge (UK) CHAIR Nerea de la Riva (Sweden) CHAIR Naomi Chikuma (Japan) Julia Maurer (USA) Tom Linnemann (Germany) Shoko Niwa (Japan) Ryo Unemoto (Japan) Elizabeth Mabrey (USA) Trustees representing RoboCupJunior Tatiana Pazelli (Brazil) Amy Eguchi (USA) Alexis Cosette Arizaga (Mexico) Fernando Ribeiro (Portugal) Gerard Elias (Australia) Gerald Steinbauer (Austria) Please read through the RoboCupJunior General Rules before proceeding on with this rule, as it is the premise for all rules. These are the official rules for RoboCupJunior Rescue Simulation 2021. They are released by the RoboCupJunior Rescue Technical Committee (TC). The English rules have priority over any translations. Additions from the 2020 rules are highlighted by a "NEW" indicator in front of the section/subsection/subsubsection, dependent on the change. Grammatical corrections, rearrangements of rules, and minor clarifications will not be indicated. It is the responsibility of the teams to have read and understood the official rules. Summary of notable changes to the rules ● Removal of the Preliminary level. All of the individual competition will be performed identically to the former Advanced level. ● Removal of the use of real robots. ● Teams are not allowed to change their AI/program after submission for each round. Final Rules as of Sept 27th, 2020 Page 1 of 31 Preface In Rescue Simulation, teams have to develop and program appropriate strategies for virtual and autonomous robots to navigate through the virtual world to collect objects while competing with another team’s robot that is searching and collecting objects in the same virtual world.