258 research outputs found
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
A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies
The robust balancing capability of humanoid robots against disturbances has
been considered as one of the crucial requirements for their practical mobility
in real-world environments. In particular, many studies have been devoted to
the efficient implementation of the three balance strategies, inspired by human
balance strategies involving ankle, hip, and stepping strategies, to endow
humanoid robots with human-level balancing capability. In this paper, a robust
balance control framework for humanoid robots is proposed. Firstly, a novel
Model Predictive Control (MPC) framework is proposed for Capture Point (CP)
tracking control, enabling the integration of ankle, hip, and stepping
strategies within a single framework. Additionally, a variable weighting method
is introduced that adjusts the weighting parameters of the Centroidal Angular
Momentum (CAM) damping control over the time horizon of MPC to improve the
balancing performance. Secondly, a hierarchical structure of the MPC and a
stepping controller was proposed, allowing for the step time optimization. The
robust balancing performance of the proposed method is validated through
extensive simulations and real robot experiments. Furthermore, a superior
balancing performance is demonstrated, particularly in the presence of
disturbances, compared to a state-of-the-art Quadratic Programming (QP)-based
CP controller that employs the ankle, hip, and stepping strategies. The
supplementary video is available at https://youtu.be/CrD75UbYzdcComment: 19 pages,13 figure
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
SISTEM KENDALI JALAN ROBOT HUMANOID PADA BIDANG TIDAK RATA MENGGUNAKAN LQR
AbstrakPengembangan robot humanoid memiliki keunggulan yaitu mobilisasi di lingkungan manusia yang baik karena strukturnya yang mirip manusia. Robot humanoid harus mampu berjalan seimbang pada bidang yang tidak rata. Bidang yang tidak rata menyebabkan adanya perubahan pola berjalan pada robot dan menybabkan robot terjatuh. Berbagai penelitian mengemukakan bahwa robot humanoid akan stabil berjalan ketika COM atau ZMP dari robot tetap berada di area telapak kaki. Kondisi tersebut dapat diwujudkan dengan menanamkan sistem kendali pada robot humanoid.Berbagai penelitian telah dilakukan untuk mendesain sistem kendali untuk robot humanoid ketika berjalan. Kendali LQR dan strategi pengenalan bidang dapat digunakan untuk menstabilkan robot humanoid namun terbatas pada permukaan bidan tertentu dan respon sistem yang tidak konsisten. Pada setiap variasi bentuk bidang jalan, robot akan memerlukan perlakuan yang berbeda.Pada penelitian ini akan dirancang kendali LQR dan strategi pengenalan bidang jalan untuk robot humanoid ketika berjalan pada bidang tidak rata. Metode LQR dipilih karena performa yang robust. Metode ini diharapkan dapat memberikan kemampuan robot humanoid untuk mengubah nilai umpan balik sistem kendali sesuai dengan keadaan robot sehingga robot dapat berjalan pada bidang tidak rata tanpa terjatuh
Motion Planning and Control of Dynamic Humanoid Locomotion
Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot.
Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d.
As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics.
The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end
Motion Planning and Control for the Locomotion of Humanoid Robot
This thesis aims to contribute on the motion planning and control problem of the locomotion
of humanoid robots. For the motion planning, various methods were proposed
in different levels of model dependence. First, a model free approach was proposed
which utilizes linear regression to estimate the relationship between foot placement
and moving velocity. The data-based feature makes it quite robust to handle modeling
error and external disturbance. As a generic control philosophy, it can be applied to
various robots with different gaits. To reduce the risk of collecting experimental data
of model-free method, based on the simplified linear inverted pendulum model, the
classic planning method of model predictive control was explored to optimize CoM
trajectory with predefined foot placements or optimize them two together with respect
to the ZMP constraint. Along with elaborately designed re-planning algorithm and
sparse discretization of trajectories, it is fast enough to run in real time and robust
enough to resist external disturbance. Thereafter, nonlinear models are utilized for
motion planning by performing forward simulation iteratively following the multiple
shooting method. A walking pattern is predefined to fix most of the degrees of the
robot, and only one decision variable, foot placement, is left in one motion plane and
therefore able to be solved in milliseconds which is sufficient to run in real time. In
order to track the planned trajectories and prevent the robot from falling over, diverse
control strategies were proposed according to the types of joint actuators. CoM stabilizer
was designed for the robots with position-controlled joints while quasi-static
Cartesian impedance control and optimization-based full body torque control were
implemented for the robots with torque-controlled joints. Various scenarios were set
up to demonstrate the feasibility and robustness of the proposed approaches, like
walking on uneven terrain, walking with narrow feet or straight leg, push recovery
and so on
Torque-Controlled Stepping-Strategy Push Recovery: Design and Implementation on the iCub Humanoid Robot
One of the challenges for the robotics community is to deploy robots which
can reliably operate in real world scenarios together with humans. A crucial
requirement for legged robots is the capability to properly balance on their
feet, rejecting external disturbances. iCub is a state-of-the-art humanoid
robot which has only recently started to balance on its feet. While the current
balancing controller has proved successful in various scenarios, it still
misses the capability to properly react to strong pushes by taking steps. This
paper goes in this direction. It proposes and implements a control strategy
based on the Capture Point concept [1]. Instead of relying on position control,
like most of Capture Point related approaches, the proposed strategy generates
references for the momentum-based torque controller already implemented on the
iCub, thus extending its capabilities to react to external disturbances, while
retaining the advantages of torque control when interacting with the
environment. Experiments in the Gazebo simulator and on the iCub humanoid robot
validate the proposed strategy
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