79 research outputs found
3LP: a linear 3D-walking model including torso and swing dynamics
In this paper, we present a new model of biped locomotion which is composed
of three linear pendulums (one per leg and one for the whole upper body) to
describe stance, swing and torso dynamics. In addition to double support, this
model has different actuation possibilities in the swing hip and stance ankle
which could be widely used to produce different walking gaits. Without the need
for numerical time-integration, closed-form solutions help finding periodic
gaits which could be simply scaled in certain dimensions to modulate the motion
online. Thanks to linearity properties, the proposed model can provide a
computationally fast platform for model predictive controllers to predict the
future and consider meaningful inequality constraints to ensure feasibility of
the motion. Such property is coming from describing dynamics with joint torques
directly and therefore, reflecting hardware limitations more precisely, even in
the very abstract high level template space. The proposed model produces
human-like torque and ground reaction force profiles and thus, compared to
point-mass models, it is more promising for precise control of humanoid robots.
Despite being linear and lacking many other features of human walking like CoM
excursion, knee flexion and ground clearance, we show that the proposed model
can predict one of the main optimality trends in human walking, i.e. nonlinear
speed-frequency relationship. In this paper, we mainly focus on describing the
model and its capabilities, comparing it with human data and calculating
optimal human gait variables. Setting up control problems and advanced
biomechanical analysis still remain for future works.Comment: Journal paper under revie
Center of Pressure Feedback for Controlling the Walking Stability Bipedal Robots using Fuzzy Logic Controller
This paper presents a sensor-based stability walk for bipedal robots by using force sensitive resistor (FSR) sensor. To perform walk stability on uneven terrain conditions, FSR sensor is used as feedbacks to evaluate the stability of bipedal robot instead of the center of pressure (CoP). In this work, CoP that was generated from four FSR sensors placed on each foot-pad is used to evaluate the walking stability. The robot CoP position provided an indication of walk stability. The CoP position information was further evaluated with a fuzzy logic controller (FLC) to generate appropriate offset angles to be applied to meet a stable situation. Moreover, in this paper designed a FLC through CoP region's stability and stable compliance control are introduced. Finally, the performances of the proposed methods were verified with 18-degrees of freedom (DOF) kid-size bipedal robot
Bipedal humanoid robot walking reference tuning by the use of evolutionary algorithms
Various aspects of humanoid robotics attracted the attention of researchers in the past four decades. One of the most challenging tasks in this area is the control of bipedal locomotion. The dynamics involved are highly nonlinear and hard to stabilize. A typical fullbody humanoid robot has more than twenty joints and the coupling effects between the links are significant. Reference generation plays a vital role for the success of the walking controller. Stability criteria including the Zero Moment Point (ZMP) criterion are extensively applied for this purpose. However, the stability criteria are usually applied on simplified models like the Linear Inverted Pendulum Model (LIPM) which only partially describes the equations of the motion of the robot. There are also trial and error based techniques and other ad-hoc reference generation techniques as well. This background of complicated dynamics and difficulties in reference generation makes automatic gait (step patterns of legged robots) tuning an interesting area of research. A natural command for a legged robot is the velocity of its locomotion. A number of walk parameters including temporal and spatial variables like stepping period and step size need to be set properly in order to obtain the desired speed. These problems, when considered from kinematics point of view, do not have a unique set of walking parameters as a solution. However, some of the solutions can be more suitable for a stable walk, whereas others may lead to instability and cause robot to fall. This thesis proposes a gait tuning method based on evolutionary methods. A velocity command is given as the input to the system. A ZMP based reference generation method is employed. Walking simulations are performed to assess the fitness of artificial populations. The fitness is measured by the amount of support the simulated bipedal robot received from torsional virtual springs and dampers opposing the changes in body orientation. Cross-over and mutation mechanisms generate new populations. A number of different walking parameters and fitness functions are tested to improve this tuning process. The walking parameters obtained in simulations are applied to the experimental humanoid platform SURALP (Sabanci University ReseArch Labaratory Platform). Experiments verify the merits of the proposed reference tuning method
Finite-time disturbance reconstruction and robust fractional-order controller design for hybrid port-Hamiltonian dynamics of biped robots
In this paper, disturbance reconstruction and robust trajectory tracking
control of biped robots with hybrid dynamics in the port-Hamiltonian form is
investigated. A new type of Hamiltonian function is introduced, which ensures
the finite-time stability of the closed-loop system. The proposed control
system consists of two loops: an inner and an outer loop. A fractional
proportional-integral-derivative filter is used to achieve finite-time
convergence for position tracking errors at the outer loop. A fractional-order
sliding mode controller acts as a centralized controller at the inner-loop,
ensuring the finite-time stability of the velocity tracking error. In this
loop, the undesired effects of unknown external disturbance and parameter
uncertainties are compensated using estimators. Two disturbance estimators are
envisioned. The former is designed using fractional calculus. The latter is an
adaptive estimator, and it is constructed using the general dynamic of biped
robots. Stability analysis shows that the closed-loop system is finite-time
stable in both contact-less and impact phases. Simulation studies on two types
of biped robots (i.e., two-link walker and RABBIT biped robot) demonstrate the
proposed controller's tracking performance and disturbance rejection
capability
Hierarchical Reactive Control for Soccer Playing Humanoid Robots
What drives thousands of researchers worldwide to devote their creativity and energy t
FUmanoid team description 2009
Abstract. This document describes hardware and software of the robots developed by the "FUmanoid" Team for the RoboCup competitions to be held in Graz, Austria 2009. The robot has 22 actuated degrees of freedom based on Dynamixel RX28, and RX64 servos. Central Processing, including Machine vision, Planning and control is performed using a Gumstix Verdex 6LP which is an ARM based 600MHz platform. Planning algorithms are organized in a new structure called Concurrent Scenario based Planning (CSBP). This paper explains the software and hardware used for the robot as well as control and stabilization methods developed by our team
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
Scaled Autonomy for Networked Humanoids
Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework.
The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment.
Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC
Analytic and Learned Footstep Control for Robust Bipedal Walking
Bipedal walking is a complex, balance-critical whole-body motion with inherently unstable inverted pendulum-like dynamics. Strong disturbances must be quickly responded to by altering the walking motion and placing the next step in the right place at the right time. Unfortunately, the high number of degrees of freedom of the humanoid body makes the fast computation of well-placed steps a particularly challenging task. Sensor noise, imprecise actuation, and latency in the sensomotoric feedback loop impose further challenges when controlling real hardware. This dissertation addresses these challenges and describes a method of generating a robust walking motion for bipedal robots. Fast modification of footstep placement and timing allows agile control of the walking velocity and the absorption of strong disturbances. In a divide and conquer manner, the concepts of motion and balance are solved separately from each other, and consolidated in a way that a low-dimensional balance controller controls the timing and the footstep locations of a high-dimensional motion generator. Central pattern generated oscillatory motion signals are used for the synthesis of an open-loop stable walk on flat ground, which lacks the ability to respond to disturbances due to the absence of feedback. The Central Pattern Generator exhibits a low-dimensional parameter set to influence the timing and the landing coordinates of the swing foot. For balance control, a simple inverted pendulum-based physical model is used to represent the principal dynamics of walking. The model is robust to disturbances in a way that it returns to an ideal trajectory from a wide range of initial conditions by employing a combination of Zero Moment Point control, step timing, and foot placement strategies. The simulation of the model and its controller output are computed efficiently in closed form, supporting high-frequency balance control at the cost of an insignificant computational load. Additionally, the sagittal step size produced by the controller can be trained online during walking with a novel, gradient descent-based machine learning method. While the analytic controller forms the core of reliable walking, the trained sagittal step size complements the analytic controller in order to improve the overall walking performance. The balanced whole-body walking motion arises by using the footstep coordinates and the step timing predicted by the low-dimensional model as control input for the Central Pattern Generator. Real robot experiments are presented as evidence for disturbance-resistant, omnidirectional gait control, with arguably the strongest push-recovery capabilities to date
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