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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.
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 Bianchi
Robot 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 Bianchi
This 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 Bianchi
This 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.
Qualitative case-based reasoning for humanoid robot soccer: A new retrieval and reuse algorithm
(2016-11-02) HOMEM, T. P. D.; PERICO, D. H.; SANTOS, P. E.; BIANCHI, R.; MANTARA, R. L. Qualitative case-based reasoning for humanoid robot soccer: A new retrieval and reuse algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 170-185,; PERICO, D. H.; SANTOS, P. E.; Reinaldo Bianchi; MANTARA, R. L.
© Springer International Publishing AG 2016.This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses a Qualitative Spatial Reasoning theory to model, retrieve and reuse cases by means of spatial relations. A qualitative distance and orientation calculus (EOPRA) is used to model cases using qualitative relations between the objects in a case. A new retrieval algorithm is proposed that uses the Conceptual Neighborhood Diagram to compute the similarity measure between a new problem and the cases in the case base. A reuse algorithm is also introduced that selects the most similar case and shares it with other agents, based on their qualitative position. The proposed approach was evaluated on simulation and on real humanoid robots. Preliminary results suggest that the proposed approach is faster than using a quantitative model and other similarity measure such as the Euclidean distance. As a result of running Q-CBR, the robots obtained a higher average number of goals than those obtained when running a metric CBR approach.
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.
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.
Solving the time lapse from vision system in a robot soccer game using kalman filter
(2019-10-25) PAULI, G.; Flávio Tonidandel
© 2019 IEEE.This article discusses some effects present in the Small Size League (SSL) category of robot soccer. The dynamic of this category requires a constant update about the state of objects in the field, however, the time to generate an update is smaller than the time between information packets arriving for teams. This latency could have a negative impact in the performance of the software, which could become limited due to the camera's capability. In order to solve this kind of obstacle, a routine was elaborated using a timer to perform the update every 1ms. The update consists of using the dynamic model of the objects combined with the Kalman filter to fill the lack of information while a new package is not available, so the software can have a reliable prediction of the object's position in the field with higher frequency.
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.
Qualitative case-based reasoning and learning
(2020-03-20) HOMEM, THIAGO PEDRO DONADON; Paulo Santos; COSTA, ANNA HELENA REALI; Reinaldo Bianchi; MANTARAS, RAMON LOPEZ DE
The development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the present paper introduces a novel algorithm for Qualitative Case-Based Reasoning and Learning (QCBRL), which is a case-based reasoning system that uses qualitative spatial representations to retrieve and reuse cases by means of relations between objects in the environment. Combined with reinforcement learning, QCBRL allows the agent to learn new qualitative cases at runtime, without assuming a pre-processing step. In order to avoid cases that do not lead to the maximum performance, QCBRL executes case-base maintenance, excluding these cases and obtaining new (more suitable) ones. Experimental evaluation of QCBRL was conducted in a simulated robot-soccer environment, in a real humanoid-robot environment and on simple tasks in two distinct gridworld domains. Results show that QCBRL outperforms traditional RL methods. As a result of running QCBRL in autonomous soccer matches, the robots performed a higher average number of goals than those obtained when using pure numerical models. In the gridworlds considered, the agent was able to learn optimal and safety policies.
On the construction of a RoboCup small size league team
(2011-01-05) Gurzoni Jr. J.A.; Martins M.F.; Flavio Tonidandel; Reinaldo Bianchi
The 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.

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