Desenvolvimento de comportamentos para robô humanoide

Mestrado em Engenharia de Computadores e TelemáticaHumanoid robotics is an area of active research. Robots with human body are better suited to execute tasks in environments designed for humans. Moreover, people feel more comfortable interacting with robots that have a human appearance. RoboCup encourages robotic research by promoting robotic competitions. One of these competitions is the Standard Platform League (SPL) in which humanoid robots play soccer. The robot used is the Nao robot, created by Aldebaran Robotics. The di erence between the teams that compete in this league is the software that controls the robots. Another league promoted by RoboCup is the 3D Soccer Simulation League (3DSSL). In this league the soccer game is played in a computer simulation. The robot model used is also the one of the Nao robot. However, there are a few di erences in the dimensions and it has one more Degree of Freedom (DoF) than the real robot. Moreover, the simulator cannot reproduce reality with precision. Both these leagues are relevant for this thesis, since they use the same robot model. The objective of this thesis is to develop behaviors for these leagues, taking advantage of the previous work developed for the 3DSSL. These behaviors include the basic movements needed to play soccer, namely: walking, kicking the ball, and getting up after a fall. This thesis presents the architecture of the agent developed for the SPL, which is similar to the architecture of the FC Portugal team agent from the 3DSSL, hence allowing to port code between both leagues easily. It was also developed an interface that allows to control a leg in a more intuitive way. It calculates the joint angles of the leg, using the following parameters: three angles between the torso and the line connecting hip and ankle; two angles between the foot and the perpendicular of the torso; and the distance between the hip and the ankle. It was also developed an algorithm to calculate the three joint angles of the hip that produce the desired vertical rotation, since the Nao robot does not have a vertical joint in the hip. This thesis presents also the behaviors developed for the SPL, some of them based on the existing behaviors from the 3DSSL. It is presented a behavior that allows to create robot movements by de ning a sequence of poses, an open-loop omnidirectional walking algorithm, and a walk optimized in the simulator adapted to the real robot. Feedback was added to this last walk to make it more robust against external disturbances. Using the behaviors presented in this thesis, the robot achieved a forward velocity of 16 cm/s, a lateral velocity of 6 cm/s, and rotated at 40 deg/s. The work developed in this thesis allows to have an agent to control the Nao robot and execute the basic low level behaviors for competing in the SPL. Moreover, the similarities between the architecture of the agent for the SPL with that of the agent from the 3DSSL allow to use the same high level behaviors in both leagues.A robótica humanoide é uma área em ativo desenvolvimento. Os robôs com forma humana estão melhor adaptados para executarem tarefas em ambientes desenhados para humanos. Além disso, as pessoas sentem-se mais confortáveis quando interagem com robôs que tenham aparência humana. O RoboCup incentiva a investigação na área da robótica através da realização de competições de robótica. Uma destas competições é a Standard Platform League (SPL) na qual robôs humanoides jogam futebol. O robô usado é o robô Nao, criado pela Aldebaran Robotics. A diferença entre as equipas que competem nesta liga está no software que controla os robôs. Outra liga presente no RoboCup é a 3D Soccer Simulation League (3DSSL). Nesta liga o jogo de futebol é jogado numa simulação por computador. O modelo de robô usado é também o do robô Nao. Contudo, existem umas pequenas diferenças nas dimensões e este tem mais um grau de liberdade do que o robô real. O simulador também não consegue reproduzir a realidade com perfeição. Ambas estas ligas são importantes para esta dissertação, pois usam o mesmo modelo de robô. O objectivo desta dissertação é desenvolver comportamentos para estas ligas, aproveitando o trabalho prévio desenvolvido para a 3DSSL. Estes comportamentos incluem os movimentos básicos necessários para jogar futebol, nomeadamente: andar, chutar a bola e levantar-se depois de uma queda. Esta dissertação apresenta a arquitetura do agente desenvolvida para a SPL, que é similar á arquitetura do agente da equipa FC Portugal da 3DSSL, para permitir uma mais fácil partilha de código entre as ligas. Foi também desenvolvida uma interface que permite controlar uma perna de maneira mais intuitiva. Ela calcula os ângulos das juntas da perna, usando os seguintes parâmetros: três ângulos entre o torso e a linha que une anca ao tornozelo; dois ângulos entre o pé e a perpendicular do torso; e a distância entre a anca e o tornozelo. Nesta dissertação foi também desenvolvido um algoritmo para calcular os três ângulos das juntas da anca que produzam a desejada rotação vertical, visto o robô Nao não ter uma junta na anca que rode verticalmente. Esta dissertação também apresenta os comportamentos desenvolvidos para a SPL, alguns dos quais foram baseados nos comportamentos já existentes na 3DSSL. É apresentado um modelo de comportamento que permite criar movimentos para o robô de nindo uma sequência de poses, um algoritmo para um andar open-loop e omnidirecional e um andar otimizado no simulador e adaptado para o robô real. A este último andar foi adicionado um sistema de feedback para o tornar mais robusto. Usando os comportamentos apresentados nesta dissertação, o robô atingiu uma velocidade de 16 cm/s para frente, 6 cm/s para o lado e rodou sobre si pr oprio a 40 graus/s. O trabalho desenvolvido nesta dissertação permite ter um agente que controle o robô Nao e execute os comportamentos básicos de baixo nível para competir na SPL. Além disso, as semelhan cas entre a arquitetura do agente para a SPL com a arquitetura do agente da 3DSSL permite usar os mesmos comportamentos de alto nível em ambas as ligas

Development of behaviors for a simulated humanoid robot

Mestrado em Engenharia de Computadores e TelemáticaControlar um robô bípede com vários graus de liberdade é um desafio que recebe a atenção de vários investigadores nas áreas da biologia, física, electrotecnia, ciências de computadores e mecânica. Para que um humanóide possa agir em ambientes complexos, são necessários comportamentos rápidos, estáveis e adaptáveis. Esta dissertação está centrada no desenvolvimento de comportamentos robustos para um robô humanóide simulado, no contexto das competições de futebol robótico simulado 3D do RoboCup, para a equipa FCPortugal3D. Desenvolver tais comportamentos exige o desenvolvimento de métodos de planeamento de trajectórias de juntas e controlo de baixo nível. Controladores PID foram implementados para o controlo de baixo nível. Para o planeamento de trajectórias, quatro métodos foram estudados. O primeiro método apresentado foi implementado antes desta dissertação e consiste numa sequência de funções degrau que definem o ângulo desejado para cada junta durante o movimento. Um novo método baseado na interpolação de um seno foi desenvolvido e consiste em gerar uma trajectória sinusoidal durante um determinado tempo, o que resulta em transições suaves entre o ângulo efectivo e o ângulo desejado para cada junta. Um outro método que foi desenvolvido, baseado em séries parciais de Fourier, gera um padrão cíclico para cada junta, podendo ter múltiplas frequências. Com base no trabalho desenvolvido por Sven Behnke, um CPG para locomoção omnidireccional foi estudado em detalhe e implementado. Uma linguagem de definição de comportamentos é também parte deste estudo e tem como objectivo simplificar a definição de comportamentos utilizando os vários métodos propostos. Integrando o controlo de baixo nível e os métodos de planeamento de trajectórias, vários comportamentos foram criados para permitir a uma versão simulada do humanóide NAO andar em diferentes direcções, rodar, chutar a bola, apanhar a bola (guarda-redes) e levantar do chão. Adicionalmente, a optimização e geração automática de comportamentos foi também estudada, utilizado algoritmos de optimização como o Hill Climbing e Algoritmos Genéticos. No final, os resultados são comparados com as equipas de simulação 3D que reflectem o estado da arte. Os resultados obtidos são bons e foram capazes de ultrapassar uma das três melhores equipas simuladas do RoboCup em diversos aspectos como a velocidade a andar, a velocidade de rotação, a distância da bola depois de chutada, o tempo para apanhar a bola e o tempo para levantar do chão. ABSTRACT: Controlling a biped robot with several degrees of freedom is a challenging task that takes the attention of several researchers in the fields of biology, physics, electronics, computer science and mechanics. For a humanoid robot to perform in complex environments, fast, stable and adaptable behaviors are required. This thesis is concerned with the development of robust behaviors for a simulated humanoid robot, in the scope of the RoboCup 3D Simulated Soccer Competitions, for FCPortugal3D team. Developing such robust behaviors requires the development of methods for joint trajectory planning and low-level control. PID control were implemented to achieve low-level joint control. For trajectory planning, four methods were studied. The first presented method was implemented before this thesis and consists of a sequence of step functions that define the target angle of each joint during the movement. A new method based on the interpolation of a sine function was developed and consists of generating a sinusoidal shape during some amount of time, leading to smooth transitions between the current angle and the target angle of each joint. Another method developed, based on partial Fourier Series, generates a multi-frequency cyclic pattern for each joint. This method is very flexible and allows to completely control the angular positions and velocities of the joints. Based on the work of developed by Sven Behnke, a CPG for omnidirectional locomotion was studied in detail and implemented. A behavior definition language is also part of this study and aims at simplifying the definition of behaviors using the several proposed methods. By integrating the low-level control and the trajectory planning methods, several behaviors were created to allow a simulated version of the humanoid NAO to walk in different directions, turn, kick the ball, catch the ball (goal keeper) and get up from the ground. Furthermore, the automatic generation of gaits, through the use of optimization algorithms such as hill climbing and genetic algorithms, was also studied and tested. In the end, the results are compared with the state of the art teams of the RoboCup 3D simulation league. The achieved results are good and were able to overcome one of the state of the art simulated teams of RoboCup in several aspects such as walking velocity, turning velocity, distance of the ball when kicked, time to catch the ball and the time to get up from the ground

GAN Hyperparameters search through Genetic Algorithm

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceRecent developments in Deep Learning are remarkable when it comes to generative models. The main reason for such progress is because of Generative Adversarial Networks (GANs) [1]. Introduced in a paper by Ian Goodfellow in 2014 GANs are machine learning models that are made of two neural networks: a Generator and a Discriminator. These two compete amongst each other to generate new, synthetic instances of data that resemble the real one. Despite their great potential, there are present challenges in their training, which include training instability, mode collapse, and vanishing gradient. A lot of research has been done on how to overcome these challenges, however, there was no significant proof found on whether modern techniques consistently outperform vanilla GAN. The performances of GANs are also highly dependent on the dataset they are trained on. One of the main challenges is related to the search for hyperparameters. In this thesis, we try to overcome this challenge by applying an evolutionary algorithm to search for the best hyperparameters for a WGAN. We use Kullback-Leibler divergence to calculate the fitness of the individuals, and in the end, we select the best set of parameters generated by the evolutionary algorithm. The parameters of the best-selected individuals are maintained throughout the generations. We compare our approach with the standard hyperparameters given by the state-of-art

Scalable allocation of safety integrity levels in automotive systems

The allocation of safety integrity requirements is an important problem in modern safety engineering. It is necessary to find an allocation that meets system level safety integrity targets and that is simultaneously cost-effective. As safety-critical systems grow in size and complexity, the problem becomes too difficult to be solved in the context of a manual process. Although this thesis addresses the generic problem of safety integrity requirements allocation, the automotive industry is taken as an application example.Recently, the problem has been partially addressed with the use of model-based safety analysis techniques and exact optimisation methods. However, usually, allocation cost impacts are either not directly taken into account or simple, linear cost models are considered; furthermore, given the combinatorial nature of the problem, applicability of the exact techniques to large problems is not a given. This thesis argues that it is possible to effectively and relatively efficiently solve the allocation problem using a mixture of model-based safety analysis and metaheuristic optimisation techniques. Since suitable model-based safety analysis techniques were already known at the start of this project (e.g. HiP-HOPS), the research focuses on the optimisation task.The thesis reviews the process of safety integrity requirements allocation and presents relevant related work. Then, the state-of-the-art of metaheuristic optimisation is analysed and a series of techniques, based on Genetic Algorithms, the Particle Swarm Optimiser and Tabu Search are developed. These techniques are applied to a set of problems based on complex engineering systems considering the use of different cost functions. The most promising method is selected for investigation of performance improvements and usability enhancements. Overall, the results show the feasibility of the approach and suggest good scalability whilst also pointing towards areas for improvement

FC Portugal 3D Simulation Team: Team Description Paper 2020

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details

Factors limiting fast bowling performance in cricket

In cricket, fast bowlers utilise the speed at which they are able to deliver the ball in order to be successful. Previous research has investigated the effect of different technique parameters on ball release speed using an experimental approach. While an experimental approach is suitable to understand the differences between bowlers it is not suitable to understand the changes required to improve a bowler’s performance. The aim of this research was to investigate the factors that limit fast bowling using a theoretical approach. A 16-segment subject-specific torque-driven computer simulation model of the front foot contact phase of fast bowling was developed, with wobbling masses included within the shank, thigh and torso representations. Torque generators were included at the MTP, ankle and knee joints on the front leg, both hip and shoulder joints, and the elbow and wrist joints on the bowling arm. [Continues.

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper