205 research outputs found
Pattern Generation for Walking on Slippery Terrains
In this paper, we extend state of the art Model Predictive Control (MPC)
approaches to generate safe bipedal walking on slippery surfaces. In this
setting, we formulate walking as a trade off between realizing a desired
walking velocity and preserving robust foot-ground contact. Exploiting this
formulation inside MPC, we show that safe walking on various flat terrains can
be achieved by compromising three main attributes, i. e. walking velocity
tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient
of Friction (RCoF) regulation. Simulation results show that increasing the
walking velocity increases the possibility of slippage, while reducing the
slippage possibility conflicts with reducing the tip-over possibility of the
contact and vice versa.Comment: 6 pages, 7 figure
From walking to running: robust and 3D humanoid gait generation via MPC
Humanoid robots are platforms that can succeed in tasks conceived for humans. From locomotion in unstructured environments, to driving cars, or working in industrial plants,
these robots have a potential that is yet to be disclosed in systematic every-day-life applications. Such a perspective, however, is opposed by the need of solving complex
engineering problems under the hardware and software point of view. In this thesis, we focus on the software side of the problem, and in particular on locomotion control. The operativity of a legged humanoid is subordinate to its capability of realizing a reliable locomotion. In many settings, perturbations may undermine the balance and make the robot fall. Moreover, complex and dynamic motions might be required by the context, as for instance it could be needed to start running or climbing stairs to achieve a certain location in the shortest time. We present gait generation schemes based on Model Predictive Control (MPC) that tackle both the problem of robustness and tridimensional dynamic motions. The proposed control schemes adopt the typical paradigm of centroidal MPC for reference motion generation, enforcing dynamic balance through the Zero Moment Point condition, plus a whole-body controller that maps the generated trajectories to joint commands. Each of the described predictive controllers also feature a so-called stability constraint, preventing the generation of diverging Center of Mass trajectories with respect to the Zero Moment Point. Robustness is addressed by modeling the humanoid as a Linear Inverted Pendulum and devising two types of strategies. For persistent perturbations, a way to use a disturbance observer and a technique for constraint tightening (to ensure robust constraint satisfaction) are presented. In the case of impulsive pushes instead, techniques for footstep and timing adaptation are introduced. The underlying approach is to interpret robustness as a MPC feasibility problem, thus aiming at ensuring the existence of a solution for the constrained optimization problem to be solved at each iteration in spite of the perturbations. This perspective allows to devise simple solutions to complex problems, favoring a reliable real-time implementation.
For the tridimensional locomotion, on the other hand, the humanoid is modeled as a Variable Height Inverted Pendulum. Based on it, a two stage MPC is introduced with particular emphasis on the implementation of the stability constraint. The overall result is a gait generation scheme that allows the robot to overcome relatively complex
environments constituted by a non-flat terrain, with also the capability of realizing running gaits. The proposed methods are validated in different settings: from conceptual simulations in Matlab to validations in the DART dynamic environment, up to experimental tests on the NAO and the OP3 platforms
Locomoção de humanoides robusta e versátil baseada em controlo analĂtico e fĂsica 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 robĂ´s humanoides sĂŁo 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 distĂşrbios 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 sĂŁo 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
analĂticas Ă© a chave para abrir as portas para robĂ´s humanoides
saĂrem dos laboratĂłrios. Propusemos um forte acoplamento entre controlo
analĂtico e aprendizagem profunda por reforço. Expandimos o
nosso controlador analĂtico com mĂłdulos de aprendizagem por reforço
para aprender como regular os parâmetros do motor de caminhada
(planeadores e controladores) de forma adaptativa e gerar resĂduos
para ajustar as posições das juntas alvo do robĂ´ (fĂsica 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 ruĂdo e impulsos externos.Programa Doutoral em Informátic
Online Bipedal Locomotion Adaptation for Stepping on Obstacles Using a Novel Foot Sensor
In this paper, we present a novel control architecture for the online
adaptation of bipedal locomotion on inclined obstacles. In particular, we
introduce a novel, cost-effective, and versatile foot sensor to detect the
proximity of the robot's feet to the ground (bump sensor). By employing this
sensor, feedback controllers are implemented to reduce the impact forces during
the transition of the swing to stance phase or steeping on inclined unseen
obstacles. Compared to conventional sensors based on contact reaction force,
this sensor detects the distance to the ground or obstacles before the foot
touches the obstacle and therefore provides predictive information to
anticipate the obstacles. The controller of the proposed bump sensor interacts
with another admittance controller to adjust leg length. The walking
experiments show successful locomotion on the unseen inclined obstacle without
reducing the locomotion speed with a slope angle of 12. Foot position error
causes a hard impact with the ground as a consequence of accumulative error
caused by links and connections' deflection (which is manufactured by
university tools). The proposed framework drastically reduces the feet' impact
with the ground.Comment: 6 pages, 2022 IEEE-RAS 21th International Conference on Humanoid
Robots (Humanoids
Desenvolvimento de comportamentos para robĂ´ 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 estĂŁo 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 robĂ´s humanoides jogam futebol. O robĂ´
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 Ă© 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 robĂ´
de nindo uma sequĂŞncia de poses, um algoritmo para um andar open-loop e
omnidirecional e um andar otimizado no simulador e adaptado para o robĂ´
real. A este Ăşltimo 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 nĂvel para competir na SPL. AlĂ©m 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 nĂvel
em ambas as ligas
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
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