124 research outputs found
Matsuoka's CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots
The desired rhythmic signals for adaptive walking of humanoid robots should have proper frequencies, phases, and shapes. Matsuoka's central pattern generator (CPG) is able to generate rhythmic signals with reasonable frequencies and phases, and thus has been widely applied to control the movements of legged robots, such as walking of humanoid robots. However, it is difficult for this kind of CPG to generate rhythmic signals with desired shapes, which limits the adaptability of walking of humanoid robots in various environments. To address this issue, a new framework that can generate desired rhythmic signals for Matsuoka's CPG is presented. The proposed framework includes three main parts. First, feature processing is conducted to transform the Matsuoka's CPG outputs into a normalized limit cycle. Second, by combining the normalized limit cycle with robot feedback as the feature inputs and setting the required learning objective, the neural network (NN) learns to generate desired rhythmic signals. Finally, in order to ensure the continuity of the desired rhythmic signals, signal filtering is applied to the outputs of NN, with the aim of smoothing the discontinuous parts. Numerical experiments on the proposed framework suggest that it can not only generate a variety of rhythmic signals with desired shapes but also preserve the frequency and phase properties of Matsuoka's CPG. In addition, the proposed framework is embedded into a control system for adaptive omnidirectional walking of humanoid robot NAO. Extensive simulation and real experiments on this control system demonstrate that the proposed framework is able to generate desired rhythmic signals for adaptive walking of NAO on fixed and changing inclined surfaces. Furthermore, the comparison studies verify that the proposed framework can significantly improve the adaptability of NAO's walking compared with the other methods
Locomoção de humanoides robusta e versátil baseada em controlo analítico e física residual
Humanoid robots are made to resemble humans but their locomotion
abilities are far from ours in terms of agility and versatility. When humans
walk on complex terrains or face external disturbances, they
combine a set of strategies, unconsciously and efficiently, to regain
stability. This thesis tackles the problem of developing a robust omnidirectional
walking framework, which is able to generate versatile
and agile locomotion on complex terrains. We designed and developed
model-based and model-free walk engines and formulated the
controllers using different approaches including classical and optimal
control schemes and validated their performance through simulations
and experiments. These frameworks have hierarchical structures that
are composed of several layers. These layers are composed of several
modules that are connected together to fade the complexity and
increase the flexibility of the proposed frameworks. Additionally, they
can be easily and quickly deployed on different platforms.
Besides, we believe that using machine learning on top of analytical approaches
is a key to open doors for humanoid robots to step out of laboratories.
We proposed a tight coupling between analytical control and
deep reinforcement learning. We augmented our analytical controller
with reinforcement learning modules to learn how to regulate the walk
engine parameters (planners and controllers) adaptively and generate
residuals to adjust the robot’s target joint positions (residual physics).
The effectiveness of the proposed frameworks was demonstrated and
evaluated across a set of challenging simulation scenarios. The robot
was able to generalize what it learned in one scenario, by displaying
human-like locomotion skills in unforeseen circumstances, even in the
presence of noise and external pushes.Os robôs humanoides são feitos para se parecerem com humanos,
mas suas habilidades de locomoção estão longe das nossas em termos
de agilidade e versatilidade. Quando os humanos caminham em
terrenos complexos ou enfrentam distúrbios externos combinam diferentes
estratégias, de forma inconsciente e eficiente, para recuperar a
estabilidade. Esta tese aborda o problema de desenvolver um sistema
robusto para andar de forma omnidirecional, capaz de gerar uma locomoção
para robôs humanoides versátil e ágil em terrenos complexos.
Projetámos e desenvolvemos motores de locomoção sem modelos e
baseados em modelos. Formulámos os controladores usando diferentes
abordagens, incluindo esquemas de controlo clássicos e ideais,
e validámos o seu desempenho por meio de simulações e experiências
reais. Estes frameworks têm estruturas hierárquicas compostas por
várias camadas. Essas camadas são compostas por vários módulos
que são conectados entre si para diminuir a complexidade e aumentar
a flexibilidade dos frameworks propostos. Adicionalmente, o sistema
pode ser implementado em diferentes plataformas de forma fácil.
Acreditamos que o uso de aprendizagem automática sobre abordagens
analíticas é a chave para abrir as portas para robôs humanoides
saírem dos laboratórios. Propusemos um forte acoplamento entre controlo
analítico e aprendizagem profunda por reforço. Expandimos o
nosso controlador analítico com módulos de aprendizagem por reforço
para aprender como regular os parâmetros do motor de caminhada
(planeadores e controladores) de forma adaptativa e gerar resíduos
para ajustar as posições das juntas alvo do robô (física residual). A
eficácia das estruturas propostas foi demonstrada e avaliada em um
conjunto de cenários de simulação desafiadores. O robô foi capaz de
generalizar o que aprendeu em um cenário, exibindo habilidades de
locomoção humanas em circunstâncias imprevistas, mesmo na presença
de ruído e impulsos externos.Programa Doutoral em Informátic
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
Development of a Locomotion and Balancing Strategy for Humanoid Robots
The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage, makes the gait unnatural, energy inefficient and exert large amounts of torque to the knee joint. Thus creating a walking engine that produces a quality and natural gait is essential for humanoid robots in general and is a factor for succeeding in RoboCup competition.
Humanoids robots are required to walk fast to be practical for various life tasks. However, its complex structure makes it prone to falling during fast locomotion. On the same hand, the robots are expected to work in constantly changing environments alongside humans and robots, which increase the chance of collisions. Several human-inspired recovery strategies have been studied and adopted to humanoid robots in order to face unexpected and avoidable perturbations. These strategies include hip, ankle, and stepping, however, the use of the arms as a recovery strategy did not enjoy as much attention. The arms can be employed in different motions for fall prevention. The arm rotation strategy can be employed to control the angular momentum of the body and help to regain balance. In this master\u27s thesis, I developed a detailed study of different ways in which the arms can be used to enhance the balance recovery of the NAO humanoid robot while stationary and during locomotion. I model the robot as a linear inverted pendulum plus a flywheel to account for the angular momentum change at the CoM. I considered the role of the arms in changing the body\u27s moment of inertia which help to prevent the robot from falling or to decrease the falling impact. I propose a control algorithm that integrates the arm rotation strategy with the on-board sensors of the NAO. Additionally, I present a simple method to control the amount of recovery from rotating the arms. I also discuss the limitation of the strategy and how it can have a negative impact if it was misused. I present simulations to evaluate the approach in keeping the robot stable against various disturbance sources. The results show the success of the approach in keeping the NAO stable against various perturbations. Finally,I adopt the arm rotation to stabilize the ball kick, which is a common reason for falling in the soccer humanoid RoboCup competitions
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
Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks
The complexity of bipedal locomotion may be attributed to the difficulty in
synchronizing joint movements while at the same time achieving high-level
objectives such as walking in a particular direction. Artificial central
pattern generators (CPGs) can produce synchronized joint movements and have
been used in the past for bipedal locomotion. However, most existing CPG-based
approaches do not address the problem of high-level control explicitly. We
propose a novel hierarchical control mechanism for bipedal locomotion where an
optimized CPG network is used for joint control and a neural network acts as a
high-level controller for modulating the CPG network. By separating motion
generation from motion modulation, the high-level controller does not need to
control individual joints directly but instead can develop to achieve a higher
goal using a low-dimensional control signal. The feasibility of the
hierarchical controller is demonstrated through simulation experiments using
the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the
controller's ability to function even without the availability of an exact
robot model.Comment: In: Proceedings of the Joint IEEE International Conference on
Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Oslo,
Norway, Aug. 19-22, 201
Optimizing simulated humanoid robot skills
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
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