Engenharia de Robôs
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Navegando Engenharia de Robôs por Autor "Flavio Tonidandel"
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Artigo de evento 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.Artigo de evento 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.Artigo de evento Analysis of WiFi localization techniques for kidnapped robot problem(2022-04-05) PEGORELLI NETO, A.; Flavio Tonidandel© 2022 IEEE.This work proposes an analysis of the earliest indoor localization techniques based on recurrent neural networks (RNN) like Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM), including k-Nearest Neighbors (KNN) machine learning, to process WiFi received signal strength data (RSS) for the kidnapped robot problem (KRP). The proposed solutions uses processed data generated in a Webots simulation of the iRobot Create robot, with the RSS signals simulated based on fingerprinting data from a real indoor area with 6 dedicated access points as reference. The efficiency of each system is evaluated using cumulative distribution function for several access point combinations, noise and vanishing levels for a model trained with the base test parameters from the reference material, with all 6 access points (APs) activated, ldBm Gaussian noise, 10% masking level and using 10 time steps of data as history inputs. The results show that RNN systems can achieve mean localization accuracy between $0.44\mathrm{m}\pm 0.39\mathrm{m}$ for LSTM and $0.50\mathrm{m}\pm 0.38\mathrm{m}$ for GRU and the KNN proposal reaching $0.68\mathrm{m}\pm 0.73\mathrm{m}$, proving the capability of those systems to recover from a KRP event keeping similar results obtained without any event.Artigo de evento Comparison and analysis of the DVG+A∗ and rapidly-exploring random trees path-planners for the robocup-small size league(2019-10-23) DA SILVA COSTA, L.; Flavio Tonidandel© 2019 IEEE.This paper provides an experimental analysis between Dynamic Visibility Graph A Star (DVG+A*) and Rapidly-exploring Random Trees (RRT) path-planners, in order to compare which one is more adequate to the scenario presented in the Small Size League (SSL). The metrics used to compare each algorithm were established based on the characteristics of a SSL game, which demand a short path, low computational cost and a safe distance from the opponent robots. For the comparison, both algorithms were tested in static and dynamic environments. After all the tests, DVG+A∗ has shown the best results.Artigo DVG+A* and RRT Path-Planners: A Comparison in a Highly Dynamic Environment(2021) DA SILVA, COSTA, L.; Flavio Tonidandel© 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.This work provides a deeper comparison between two path planning algorithms, the Dynamic Visibility Graph A Star (DVG+A*) and Rapidly–exploring Random Trees (RRT), when applied in a high dimension and dynamic environment, which is the RoboCup Small Size League. The algorithms were compared under two different perspectives. In the first analysis, the algorithms were evaluated according to its computational time, path length and path safety in a static environment. Afterwards, they were evaluated regarding the accumulated computational time, number of recalculated paths, total navigation time and number of collisions in a dynamic environment. The static environment results have shown that the DVG+A* has a better overall performance than RRT, except for the path safety, however, some ideas on how to improve this were discussed. In the dynamic environment the algorithms performed similarly and with a high number of collisions during the experiments. Thus, showing the importance of using an obstacle avoidance algorithm combined with the path planner. In conclusion, the results obtained showed that both algorithms aren’t suitable for highly dynamic and cluttered environments, however, due how sparse the obstacles are in the SSL, they can still be used with some care. Regarding static environments, the DVG+A* has shown the best results.Artigo de evento 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.Artigo de evento 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.Artigo de evento 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.Artigo On the construction of a RoboCup small size league team(2011-01-05) Gurzoni Jr. J.A.; Martins M.F.; Flavio Tonidandel; Reinaldo BianchiThe Robot Soccer domain has become an important artificial intelligence test bench and a widely studied research area. It is a domain with real, dynamic, and uncertain environment, where teams of robots cooperate and face adversarial competition. To build a RoboCup Small Size League (SSL) team able to compete in the world championship requires multidisciplinary research in fields like robotic hardware development, machine learning, multi-robot systems, computer vision, control theory, and mechanics, among others. This paper intends to provide insights about the aspects involved on the development of the RoboFEI RoboCup SSL robot soccer team and to present the contributions produced over its course. Among these contributions, a computer vision system employing an artificial neural network (ANN) to recognize colors, a heuristic algorithm to recognize partially detected objects, an implementation of the known rapidly-exploring random trees (RRT) path planning algorithm with additional rules, enabling the angle of approach of the robot to be controlled, and a layered strategy software system. Experimental results on real robots demonstrate the high performance of the vision system and the efficiency of the RRT algorithm implementation. Some strategy functions are also experimented, with empirical results showing their effectiveness. © 2011 The Brazilian Computer Society.Artigo de evento Performing and blocking passes in small size league(2019-10-25) LAUREANO, M. A. P.; Flavio Tonidandel© 2019 IEEE.The changes in the Small Size League rules, like increasing the field size and inclusion of more robots in the game have brought greater possibilities of playing and strategies. With the increased complexity of soccer matches, the positioning of the robots has become important as the defense and attack mechanisms. The learning of opposing team game playing has been shown to be effective, but an SSL soccer match indicates the need for solutions that analyze the strategy of the opposing team during the game and make necessary adaptations. This paper proposes the use of the Particle Swarm Optimization (PSO) algorithm as an option to determine the positioning for making and blocking passes during the match. A prototype has been developed to validate the configuration parameters. Experiments in a simulator and analysis of game logs have demonstrated the feasibility of applying the PSO algorithm to find the robots positions.