Engenharia de Robôs
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/339
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15 resultados
Resultados da Pesquisa
- A Case Base Seeding for Case-Based Planning Systems(2004-12-26) Flavio Tonidandel; RILLO, M.This paper describes a Case Base Seeding system (CBS) that can be used to seed a case base with some random cases in order to provide minimal conditions for the empirical tests of a Case-Based Planning System (CBP). Random case bases are necessary to guarantee that the results of the tests are not manipulated. Although these kind of case bases are important, there are no references about CBS systems in the literature even from those CBP systems that claim to use some similar systems. Therefore, this paper tries to overcome this deficiency by modeling and implementing a complete random Case Base Seeding process. © Springer-Verlag Berlin Heidelberg 2004.
- A fast model-based vision system for a robot soccer team(2006-11-17) MARTINS, M. F.; Flavio Tonidandel; Reinaldo BianchiRobot Soccer is a challenging research domain for Artificial Intelligence, which was proposed in order to provide a long-term problem in which researchers can investigate the construction of systems involving multiple agents working together in a dynamic, uncertain and probabilistic environment, to achieve a specific goal. This work focuses on the design and implementation of a fast and robust computer vision system for a team of small size robot soccer players. The proposed system combines artificial intelligence and computer vision techniques to locate the mobile robots and the ball, based on global vision images. To increase system performance, this work proposes a new approach to interpret the space created by a well-known computer vision technique called Hough Transform, as well as a fast object recognition method based on constraint satisfaction techniques. The system was implemented entirely in software using an off-the-shelf frame grabber. Experiments using real time image capture allows to conclude that the implemented system are efficient and robust to noises and lighting variation, being capable of locating all objects in each frame, computing their position and orientation in less than 20 milliseconds. © Springer-Verlag Berlin Heidelberg 2006.
- Learning to select object recognition methods for autonomous mobile robots(2008-07-25) Reinaldo Bianchi; RAMISA, A.; MANTARAS, R. L .© 2008 The authors and IOS Press. All rights reserved.Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and intuition, not systematically taking into account information that can be found in the images and in the visual process itself to learn which algorithm should be used, in execution time. This paper presents a method that uses Reinforcement Learning to decide which algorithm should be used to recognize objects seen by a mobile robot in an indoor environment, based on simple attributes extracted on-line from the images, such as mean intensity and intensity deviation. Two state-of-the-art object recognition algorithms can be selected: the constellation method proposed by Lowe together with its interest point detector and descriptor, the Scale-Invariant Feature Transform and a bag of features approach. A set of empirical evaluations was conducted using a household mobile robots image database, and results obtained shows that the approach adopted here is very promising.
- Market-based dynamic task allocation using heuristically accelerated reinforcement learning(2011-10-10) GURZONI JUNIOR, J. A.; Flavio Tonidandel; Reinaldo BianchiThis paper presents a Multi-Robot Task Allocation (MRTA) system, implemented on a RoboCup Small Size League team, where robots participate of auctions for the available roles, such as attacker or defender, and use Heuristically Accelerated Reinforcement Learning to evaluate their aptitude to perform these roles, given the situation of the team, in real-time. The performance of the task allocation mechanism is evaluated and compared in different implementation variants, and results show that the proposed MRTA system significantly increases the team performance, when compared to pre-programmed team behavior algorithms. © 2011 Springer-Verlag.
- Heuristically-accelerated reinforcement learning: A comparative analysis of performance(2014) MARTINS, M. F.; Reinaldo BianchiThis paper presents a comparative analysis of three Reinforcement Learning algorithms (Q-learning, Q(λ)-learning and QS-learning) and their heuristically-accelerated variants (HAQL, HAQ(λ) and HAQS) where heuristics bias action selection, thus speeding up the learning. The experiments were performed in a simulated robot soccer environment which reproduces the conditions of a real competition league environment. The results clearly demonstrate that the use of heuristics substantially improves the performance of the learning algorithms. © 2014 Springer-Verlag.
- Five years of SSL-vision - Impact and development(2014) ZICKLER, S.; LAUE, T.; GURZONI JR, J. A.; BIRBACH, O.; BISWAS, J.; VELOSO, M.Since its start in 1997, the setup of the RoboCup Small Size Robot League (SSL) enabled teams to use their own cameras and vision algorithms. In the fast and highly dynamic SSL environment, researchers achieved significant algorithmic advances in real-time complex colored-pattern based perception. Some teams reached, published, and shared effective solutions, but for new teams, vision processing has still been a heavy investment. In addition, it became an organizational burden to handle the multiple cameras from all the teams. Therefore, in 2008, the league started the development of a centralized, shared vision system, called SSL-Vision, which would be provided for all teams. In this paper, we discuss this system's successful implementation in SSL itself, but also beyond it in other domains. SSL-Vision is an open source system available to any researcher interested in processing colored patterns from static cameras. © 2014 Springer-Verlag Berlin Heidelberg.
- Hardware and software aspects of the design and assembly of a new humanoid robot for RoboCup soccer(2014-10-23) PERICO, D. H.; SILVA, I. J.; VILAO, C. O.; HOMEM, T. P. D.; DESTRO. R. C.; Flávio Tonidandel; Reinaldo Bianchi© 2014 IEEE.This paper describes the design and development of a new humanoid robot named Newton, that is intended for applications in research and also to be used in the Robo Cup Kid Size League World Competition. Newton robot has been designed to work without any dedicated sub-controller implemented in low level hardware, often used to control the servomotors of the robot. Newton uses only a standard personal computer to do all processing and control necessary by the robot. To be able to deal with all the tasks involved in the robotic soccer domain, a new software architecture is proposed. This architecture is based on the hybrid paradigm, involving sensing, decision, planning, low level control, localization and communication. Preliminary tests show that the robot can walk properly while it performs tasks like finding the ball in an unknown position or positioning itself at the ball for kicking, exhibiting a very good performance.
- Newton: A high level control humanoid robot for the robocup soccer kidsize league(2015-01-05) PERICO, D. H.; SILVA, I. J.; VILAO JUNIOR, C. O.; HOMEM, T. P. D.; DESTRO, R. C.; Flavio Tonidandel; Reinaldo Bianchi© Springer-Verlag Berlin Heidelberg 2015.One of the goals of humanoid robot researchers is to develop a complete – in terms of hardware and software – artificial autonomous agent able to interact with humans and to act in the contemporary world, that is built for human beings. There has been an increasing number of humanoid robots in the last years, including Aldebaran’s NAO and Romeo, Intel’s Jimmy and Robotis’ DARwIn-OP. This research article describes the project and development of a new humanoid robot named Newton, made for research purposes and also to be used in the RoboCup Soccer KidSize League Competition. Newton robot’s contributions include that it has been developed to work without a dedicated microcontroller board, using an four-by-four-inch Intel NUC board, that is a fully functioning PC. To work with this high level hardware, a new software architecture comprised of completely independent processes was proposed. This architecture, called Cross Architecture, is comprised of completely independent processes, one for each intelligent system required by a soccer player: Vision, Localization, Decision, Communication, Planning, Sense and Acting, besides having a process used for managing the others. The experiments showed that the robot could walk, find the ball in an unknown position, recover itself from a fall and kicking the ball autonomously with a good performance.
- Humanoid robot gait on sloping floors using reinforcement learning(2016-01-05) SILVA, I. J.; PERICO, D. H.; HOMEM, T. P. D.; VILAO, C. O.; Reinaldo Bianchi; Flavio Tonidandel© Springer International Publishing AG 2016.Climbing ramps is an important ability for humanoid robots: ramps exist everywhere in the world, such as in accessibility ramps and building entrances. This works proposes the use of Reinforcement Learning to learn the action policy that will make a robot walk in an upright position, in a lightly sloped terrain. The proposed architecture of our system is a two-layer combination of the traditional gait generation control loop with a reinforcement learning component. This allows the use of an accelerometer to generate a correction for the gait, when the slope of the floor where the robot is walking changes. Experiments performed on a real robot showed that the proposed architecture is a good solution for the stability problem.
- Evaluating the performance of two computer vision techniques for a mobile humanoid agent acting at Robocup kidsized soccer league(2016-10-31) VILAO, C. O.; FERREIRA, V. N.; CELIBERTO, L. A.; Reinaldo Bianchi© Springer International Publishing AG 2016.A humanoid robot capable of playing soccer needs to identify several objects in the soccer field in order to play soccer. The robot has to be able to recognize the ball, teammates and opponents, inferring information such as their distance and estimated location. In order to achieve this key requisite, this paper analyzes two descriptor algorithms, HAAR and HOG, so that one of them can be used for recognizing humanoid robots with less false positives alarms and with best frame per second rate. They were used with their respective classical classifiers, AdaBoost and SVM. As many different robots are available in RoboCup domain, the descriptor needs to describe features in a way that they can be distinguished from the background at the same time the classification has to have a good generalization capability. Although some limitations appeared in tests, the results were beyond expectations. Given the results, the chosen descriptor should be able to identify a mainly white-ball, which is clearly a simpler object. The results for ball detection were also quite interesting.