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Hardware and Software Aspects of the Design and Assembly of a New Humanoid Robot for RoboCup Soccer
Abstract
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 RoboCup KidSize 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.
... As sensors, it uses an UM7 Ultra-Miniature Orientation Sensor and a Logitech HD Pro Webcam C920 (Full HD). And the computer used is an Intel NUC 4 Core i5-4250U, 8GB SDRAM, 120GB SDD [4,38]. In order to increase the field of view of the camera and eliminate the pan and tilt servo motors, the robot camera is equipped with a fish-eye lens. ...
This paper investigates the use of Deep Reinforcement Learning (DRL) applied to the humanoid robot soccer environment, where a robot must learn from basic to complex skills while it interacts with the environment through images received by its own camera. To do so, the Dueling Double DQN algorithm is used: it receives the images from the robot's camera and decides on which discrete action should be performed, such as walk forward, turn to the left or kick the ball. The first experiments were performed in a robotic simulator in which the robot could learn, with DRL, three different tasks: to walk towards the ball, to act like a penalty taker and to act like a goalkeeper. In the second experiment, the learning obtained in the task to walk towards the ball was transferred to a real humanoid robot and a similar behavior could be observed, even though the environment was not exactly the same when the domain was changed. Results showed that it is possible to use DRL to learn tasks related to the role of a humanoid robot-soccer player, such as goalkeeper and penalty taker.
... Most of techniques used by the teams presented in the domain, relies in developing separately vision and decision modules and then interconnect both ( [15], [5], [13]). This strategy works, however in order to overcome the computer *This work was supported by CAPES organization 1 limitations, lighter techniques are being used. ...
One of the challenges of Deep Learning research is to develop algorithms for mobile robotic agents that operate in uncontrolled environments, in which dynamic changes and limited processing power are common restrictions. A common solution is to develop separate vision and decision modules, so that the former is based on deep neural network architectures and the latter is based on rules, and then interconnect them. The drawback of this solution is that the modules need to exchange high-level information about the objects in the scene, which are usually the positions of all objects in the scene, and this is computationally expensive. To address this problem, this paper presents a Decision Tree of Deep Neural Networks (DT-DNN) that aims to perform end to end—from image to decision—processing, and, thus, eliminating the need for quantitative and relational information about the image. This model is composed of smaller and more specialized modular DNNs, thus solving the trade-off between performance and inference time. Experiments were carried out using a real robot in the RoboCup Humanoid League domain in a soccer field, and also in simulation. We compared DT-DNN with several traditional DNN architectures. From the results, it is possible to conclude that the use of the DT-DNN made the system simpler and more robust, with fewer parameters to be adjusted, reducing the time spent with inference and also increasing the performance when compared to the traditional approach.
... Todos os seus processos são executados em um único computador, incluindo o controle dos motores. Assim, para lidar com a execução de todos os processos necessários -visão, localização, decisão, comunicação, planejamento e controle de movimentos -ao mesmo tempo, uma nova arquitetura híbrida de software foi proposta porPerico et al. (2014).A Arquitetura em Cruz (PERICO et al., 2014), como foi chamada essa nova arquitetura, pode ser vista na Figura 40, na qual cada caixa da imagem representa um processo completa-Tabela 4 -Características dos Robôs mente independente para o computador e n representa o número do robô. Essa arquitetura traz algumas vantagens, como por exemplo, o fato dos processos poderem trabalhar em paralelo e em linguagens diferentes de programação. ...
... Today, single-board computers, e.g. Raspberry Pi [Upton and Halfacree, 2014], and Intel NUCs [Perico et al., 2014] are mostly used, depending on the size of the robot. All these boards provide GPUs and multiple CPU cores. ...
Sharing software modules between teams in the RoboCup Humanoid League is difficult since all teams use different frameworks. This leads to reimplementation of software which slows the research process. A common framework for the league would resolve this. Therefore, this thesis proposes a ROS-based architecture which is defined by a set of ROS messages. The teams can decide on the specific implementation of nodes since the messages provide the interfaces. Different tools, especially for visualization, are implemented to be used in conjunction with this architecture. Furthermore, the robot control module of the team Hamburg Bit-Bots is transferred into the new framework to show its usability. The architecture is compared to others and its performance is evaluated. The presented architecture makes sharing software modules easier and can thereby accelerate the research in the RoboCup Humanoid League. Furthermore, the entry of new teams is simplified, due to the availability of shared modules.
... Most of techniques used by the teams presented in the domain, relies in developing separately vision and decision modules and then interconnect both ( [15], [5], [13]). This strategy works, however in order to overcome the computer *This work was supported by CAPES organization 1 limitations, lighter techniques are being used. ...
Developing a vision system combined with a decision system for a humanoid robot, capable of playing soccer in the RoboCup domain, has been proved to be a challenging task. The computational limitations imposed by a embedded computer inside the robot and special conditions, such as the use of colored objects, led teams to use techniques based on color segmentation for vision and conditional statements for decision. However, the current league trend is to insert the robots into more and more realistic environments. This will require the robot to, given an image provided by its camera, to abstract all the information it needs to make a decision regardless of the environment. Most robotic vision systems at RoboCup relies on traditional computer vision techniques: thresholding; windowing; segmentation; and classification that requires hours of labeling to training and testing. This paper proposes a system that does not require to locate objects coordinates in the image-a deep neural network will identify most important features resulting as an output that is a decision. Results show that Deep Neural Network (DNNs) enabled the system to be more simple, robust (with less parameters to be set by hand) and achieved a performance that is compatible with the dynamics of the humanoid robot soccer. This system was tested in a real robot and simulator.
... Both simulation and real robot experiments were conducted using software developed with the purpose of enabling the reproduction of experiments and performance comparison of different algorithms: the RoboFEI Humanoid Soccer Simulator. This software uses the Cross architecture described in Perico et al. [29], which is based on low-level tasks, such as vision, control and communication processes, allowing users to develop and test high-level decision-making algorithms in simulation and transfer them to real robots without the need of much software modifications. The Cross architecture (Fig. 8) is a hybrid architecture, because there are some aspects of reactive and hierarchical paradigms. ...
This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses Qualitative Spatial Reasoning theory to model, retrieve and reuse cases by means of spatial relations. Qualitative relations between objects, represented in terms of the EOPRA formalism, are stored as qualitative cases that are applied in the definition of new retrieval and reuse algorithms. The retrieval algorithm uses a Conceptual Neighborhood Diagram to compute the similarity between a new problem and the cases in the case base, and to select the most similar case. The reuse algorithm uses a composition algorithm to calculate the adapted position of the agents based on their frame of reference. The proposed approach was evaluated on simulation and on real humanoid robots. Results suggest that this proposal is faster than using a quantitative model with a numerical similarity measurement 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.
... Simulation experiments were conducted using a software developed with the purpose of enabling the reproduction of experiments and performance comparison of different algorithms in the literature: the RoboFEI Humanoid Soccer Simulator. This simulator uses the Cross architecture described in [22], which implements low-level processes, such as vision, control and communication processes, allowing users to develop and test high-level AI algorithms -as collective strategies or decision-making processes -in simulation. The simulator also facilitates the code to be transferred to real robots without the need of many modifications. ...
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 () 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.
... The main difference between the robot used in this experiment and the DARwIn-OP is that our robot is using the Intel NUC i5 processing board, instead of FitPC (present in the original DARwIn-OP project) and a new electronics and software architecture, that is presented in [22]. The robot, that have been developed by RoboFEI-HT team to participate in the RoboCup Humanoid KidSize League, can be seen in Fig. 4. The robot has 20 DOF, being 6 per leg, 3 per arm, 2 on the head, height 490 mm, weight 3.0 Kg, walking speed 10 cm/s. ...
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.
This paper presents a humanoid robot framework, composed of a simulator and a telemetry interface. The framework is based on the Cross Architecture, and it is developed aiming for the RoboCup Soccer Humanoid League domain. A simulator is an important tool for testing cognitive algorithms without handling issues of real robots; furthermore, a simulator is extremely useful for allowing reproducibility of any developed algorithm, even if there is no robot available. The proposed simulator allows an easy transfer of the algorithms developed in the simulator to real robots, as long as it uses the Cross Architecture as its software architecture. Then, in order to evaluate the cognitive algorithms in real robots, a telemetry interface is proposed. From this interface, it is possible to monitor any variable in the robot’s shared memory. The framework is open source and has low computational cost. Experiments were conducted in order to analyze both, simulator and telemetry interface. Experiments performed with the simulator aim to validate the high-level strategy development and the portability to a real robot, while experiments with telemetry interface aim to evaluate the robot behavior using, as input, the information received from the shared memory passed by all processes. The results show that the simulator can be used to test and develop new algorithms, while the telemetry can be used to monitor the robot, thus validating the framework for this domain.
This article offers an alternative perspective on organizational cognition based on e-cognition whereby appeal to systemic cognition replaces the traditional computational model of the mind that is still extremely popular in organizational research. It uses information processing, not to explore inner processes, but as the basis for pursuing organizational matters. To develop a theory of organizational cognition, the current work presents an agent-based simulation model based on the case of how individual perception of scientific value is affected by and affects organizational intelligence units’ (e.g., research groups’, departmental) framing of the notorious impact factor. Results show that organizational cognition cannot be described without an intermediate meso scale – called here social organizing – that both filters and enables the many kinds of socially enabled perception, action and behavior that are so characteristic of human cognition.
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.
In order to perform a walk on a real environment, humanoid robots need to adapt themselves to the environment, as humans do. One approach to achieve this goal is to use Machine Learning techniques that allow robots to improve their behavior with time. In this paper, we propose a system that uses Reinforcement Learning to learn the action policy that will make a robot walk in an upright position, in a lightly sloped terrain. To validate this proposal, experiments were made with a humanoid robot -- a robot for the RoboCup Humanoid League based on DARwIn-OP. The results showed that the robot was able to walk on sloping floors, going up and down ramps, even in situations where the slope angle changes.
This paper presents the design method for a humanoid which has a network based modular structure and a standard PC architecture. Based on the proposed method, we developed DARwIn-OP which meets the requirements for an open humanoid platform. DARwIn-OP has an expandable system structure, high performance, simple maintenance, familiar development environment, and affordable prices. All resources of DARwIn-OP including source codes, circuit diagrams, mechanical CAD files, and parts information will be opened to the public.
In the area of biped robot research, much progress has been made in the past few years. However, some difficulties remain to be dealt with, particularly about the implementation of fast and dynamic walking gaits, in other words anthropomorphic gaits, especially on uneven terrain. In this perspective, both concepts of center of pressure (CoP) and zero moment point (ZMP) are obviously useful. In this paper, the two concepts are strictly defined, the CoP with respect to ground-feet contact forces, the ZMP with respect to gravity plus inertia forces. Then, the coincidence of CoP and ZMP is proven, and related control aspects are examined. Finally, a virtual CoP-ZMP is defined, allowing us to extend the concept when walking on uneven terrain. This paper is a theoretical study. Experimental results are presented in a companion paper, analyzing the evolution of the ground contact forces obtained from a human walker wearing robot feet as shoes.
This paper reviews key concepts of the Autonomous Robot Architecture (AuRA). Its structure, strengths, and roots in biology are presented. AuRA is a hybrid deliberative/reactive robotic architecture that has been developed and refined over the past decade. In this article, particular focus is placed on the reactive behavioral component of this hybrid architecture. Various real world robots that have been implemented using this architectural paradigm are discussed, including a case study of a multiagent robotic team that competed and won the 1994 AAAI Mobile Robot Competition. 1 Introduction The Autonomous Robot Architecture (AuRA) was developed in the mid-1980's as a hybrid approach to robotic navigation [6]. Hybridization arises from the presence of two distinct components: a deliberative or hierarchical planner, based on traditional artificial intelligence techniques; and a reactive controller, based upon schema theory [2]. It was the first robot navigational system to be pres...
Journal of Experimental and Theoretical Artificial Intelligence (JETAI) 9, 1997, 215-235. Special issue on Architectures for Physical Agents. Mobile robots, if they areto perform useful tasks andbecome accepted in open environments, must be fully autonomous. Autonomy has many different aspects; here we concentrate on three central ones: the ability to attend to another agent, to take advice about the environment, and to carry out assigned tasks. All three involve complex sensing and planning operations on the part of the robot, including the use of visual tracking of humans, coordination of motor controls, and planning. We show how these capabilities are integrated in the Saphira architecture, using the concepts of coordination of behavior, coherence of modeling, and communication with other agents. This paper reports work done while this author was at SRI International. 1 Autonomous Mobile Agents What are the minimal capabilities for an autonomous mobile agent? Posed in th...
A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
The control problem—which of its potential actions should an AI system perform at each point in the problem-solving process?—is fundamental to all cognitive processes. This paper proposes eight behavioral goals for intelligent control and a ‘blackboard control architecture’ to achieve them. The architecture distinguishes domain and control problems, knowledge, and solutions. It enables AI systems to operate upon their own knowledge and behavior and to adapt to unanticipated problem-solving situations. The paper shows how opm, a blackboard control system for multiple-task planning, exploits these capabilities. It also shows how the architecture would replicate the control behavior of hearsay-ii and hasp. The paper contrasts the blackboard control architecture with three alternatives and shows how it continues an evolutionary progression of control architectures. The paper concludes with a summary of the blackboard control architecture's strengths and weaknesses.
Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporate—everything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments.
We investigate several basic problems in vision under the assumption that the observer is active. An observer is called active when engaged in some kind of activity whose purpose is to control the geometric parameters of the sensory apparatus. The purpose of the activity is to manipulate the constraints underlying the observed phenomena in order to improve the quality of the perceptual results. For example a monocular observer that moves with a known or unknown motion or a binocular observer that can rotate his eyes and track environmental objects are just two examples of an observer that we call active. We prove that an active observer can solve basic vision problems in a much more efficient way than a passive one. Problems that are ill-posed and nonlinear for a passive observer become well-posed and linear for an active observer. In particular, the problems of shape from shading and depth computation, shape from contour, shape from texture, and structure from motion are shown to be much easier for an active observer than for a passive one. It has to be emphasized that correspondence is not used in our approach, i.e., active vision is not correspondence of features from multiple viewpoints. Finally, active vision here does not mean active sensing, and this paper introduces a general methodology, a general framework in which we believe low-level vision problems should be addressed.
This paper presents an embedded control architecture constructed for Robo-Erectus, a soccer-playing humanoid robot developed at the Advanced Robotics and Intelligent Control Centre of Singapore Polytechnic. The Robo-Erectus team has participated in the KidSize category of RoboCup's Humanoid League since 2002, collecting different awards. The latest version of Robo-Erectus has many capabilities that can be exploited to improve the robot's behavior. The new embedded controller has made possible the first stage of the performance (displayed during RoboCup 2007), including network communication, mapping, and localization. The new mechanical, electronic design, embedded control architecture, and control schemes are described in this paper. In addition to the hardware, the paper presents details of the modules for gait generation, vision, behavior control, and communication.
First Page of the Article
Construcao de um robo humanoide para humanoid league - futebol de robos
- M P Cortez
- R A C Bianchi
RX-28's Manual Available: print
- Robotis-Dynamixel
ConstruçConstruç˜Construção de um robô humanoide para humanoid league-futebol de robôs
- M P Cortez Jr
- R A C Bianchi
M. P. Cortez Jr and R. A. C. Bianchi, "ConstruçConstruç˜Construção de um
robô humanoide para humanoid league-futebol de robôs,"
in 1 o Simpósio de IniciaçIniciaç˜Iniciação Científica da FEI (SICFEI),
São Bernardo do Campo, 2011.
RX-28's Manual, Accessed: 201401-16
- Robotis-Dynamixel
Robotis-Dynamixel, RX-28's Manual, Accessed: 201401-16. http://support.robotis.com/en/product/dynamixel/rx
series/rx-28.htm.
Construção de um robô humanoide para humanoid league -futebol de robôs," in 1 o Simpósio de Iniciação Científica da FEI (SICFEI)
- M P Cortez
- R A C Bianchi
M. P. Cortez Jr and R. A. C. Bianchi, "Construção de um
robô humanoide para humanoid league -futebol de robôs,"
in 1 o Simpósio de Iniciação Científica da FEI (SICFEI),
São Bernardo do Campo, 2011.