2,989 research outputs found
Design, modeling and implementation of a soft robotic neck for humanoid robots
Mención Internacional en el título de doctorSoft humanoid robotics is an emerging field that combines the flexibility and safety of soft
robotics with the form and functionality of humanoid robotics. This thesis explores the potential
for collaboration between these two fields with a focus on the development of soft joints for the
humanoid robot TEO. The aim is to improve the robot’s adaptability and movement, which are
essential for an efficient interaction with its environment.
The research described in this thesis involves the development of a simple and easily transportable
soft robotic neck for the robot, based on a 2 Degree of Freedom (DOF) Cable Driven
Parallel Mechanism (CDPM). For its final integration into TEO, the proposed design is later
refined, resulting in an efficiently scaled prototype able to face significant payloads.
The nonlinear behaviour of the joints, due mainly to the elastic nature of their soft links,
makes their modeling a challenging issue, which is addressed in this thesis from two perspectives:
first, the direct and inverse kinematic models of the soft joints are analytically studied,
based on CDPM mathematical models; second, a data-driven system identification is performed
based on machine learning techniques. Both approaches are deeply studied and compared, both
in simulation and experimentally.
In addition to the soft neck, this thesis also addresses the design and prototyping of a soft
arm capable of handling external loads. The proposed design is also tendon-driven and has a
morphology with two main bending configurations, which provides more versatility compared
to the soft neck.
In summary, this work contributes to the growing field of soft humanoid robotics through
the development of soft joints and their application to the humanoid robot TEO, showcasing the
potential of soft robotics to improve the adaptability, flexibility, and safety of humanoid robots.
The development of these soft joints is a significant achievement and the research presented in this thesis paves the way for further exploration and development in this field.La robótica humanoide blanda es un campo emergente que combina la flexibilidad y seguridad
de la robótica blanda con la forma y funcionalidad de la robótica humanoide. Esta
tesis explora el potencial de colaboración entre estos dos campos centrándose en el desarrollo
de una articulación blanda para el cuello del robot humanoide TEO. El objetivo es mejorar la
adaptabilidad y el movimiento del robot, esenciales para una interacción eficaz con su entorno.
La investigación descrita en esta tesis consiste en el desarrollo de un prototipo sencillo
y fácilmente transportable de cuello blando para el robot, basado en un mecanismo paralelo
actuado por cable de 2 grados de libertad. Para su integración final en TEO, el diseño propuesto
es posteriormente refinado, resultando en un prototipo eficientemente escalado capaz de manejar
cargas significativas.
El comportamiemto no lineal de estas articulaciones, debido fundamentalmente a la naturaleza
elástica de sus eslabones blandos, hacen de su modelado un gran reto, que en esta tesis
se aborda desde dos perspectivas diferentes: primero, los modelos cinemáticos directo e inverso
de las articulaciones blandas se estudian analíticamente, basándose en modelos matemáticos de
mecanismos paralelos actuados por cable; segundo, se aborda el problema de la identificación
del sistema mediante técnicas basadas en machine learning. Ambas propuestas se estudian y
comparan en profundidad, tanto en simulación como experimentalmente.
Además del cuello blando, esta tesis también aborda el diseño de un brazo robótico blando
capaz de manejar cargas externas. El diseño propuesto está igualmente basado en accionamiento
por tendones y tiene una morfología con dos configuraciones principales de flexión, lo que
proporciona una mayor versatilidad en comparación con el cuello robótico blando.
En resumen, este trabajo contribuye al creciente campo de la robótica humanoide blanda
mediante el desarrollo de articulaciones blandas y su aplicación al robot humanoide TEO, mostrando el potencial de la robótica blanda para mejorar la adaptabilidad, flexibilidad y seguridad
de los robots humanoides. El desarrollo de estas articulaciones es una contribución
significativa y la investigación presentada en esta tesis allana el camino hacia nuevos desarrollos
y retos en este campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Cecilia Elisabet García Cena.- Secretario: Dorin Sabin Copaci.- Vocal: Martin Fodstad Stole
Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The neck is an important part of the body that connects the head to the torso, supporting the weight and generating the movement of the head. In this paper, a cable-driven parallel platform with a pneumatic muscle active support (CPPPMS) is presented for imitating human necks, where cable actuators imitate neck muscles and a pneumatic muscle actuator imitates spinal muscles, respectively. Analyzing the stiffness of the mechanism is carried out based on screw theory, and this mechanism is optimized according to the stiffness characteristics. While taking the dynamics of the pneumatic muscle active support into consideration as well as the cable dynamics and the dynamics of the Up-platform, a dynamic modeling approach to the CPPPMS is established. In order to overcome the flexibility and uncertainties amid the dynamic model, a sliding mode controller is investigated for trajectory tracking, and the stability of the control system is verified by a Lyapunov function. Moreover, a PD controller is proposed for a comparative study. The results of the simulation indicate that the sliding mode controller is more effective than the PD controller for the CPPPMS, and the CPPPMS provides feasible performances for operations under the sliding mode control
A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions
A long-lasting goal of robotics research is to operate robots safely, while
achieving high performance which often involves fast motions. Traditional
motor-driven systems frequently struggle to balance these competing demands.
Addressing this trade-off is crucial for advancing fields such as manufacturing
and healthcare, where seamless collaboration between robots and humans is
essential. We introduce a four degree-of-freedom (DoF) tendon-driven robot arm,
powered by pneumatic artificial muscles (PAMs), to tackle this challenge. Our
new design features low friction, passive compliance, and inherent impact
resilience, enabling rapid, precise, high-force, and safe interactions during
dynamic tasks. In addition to fostering safer human-robot collaboration, the
inherent safety properties are particularly beneficial for reinforcement
learning, where the robot's ability to explore dynamic motions without causing
self-damage is crucial. We validate our robotic arm through various
experiments, including long-term dynamic motions, impact resilience tests, and
assessments of its ease of control. On a challenging dynamic table tennis task,
we further demonstrate our robot's capabilities in rapid and precise movements.
By showcasing our new design's potential, we aim to inspire further research on
robotic systems that balance high performance and safety in diverse tasks. Our
open-source hardware design, software, and a large dataset of diverse robot
motions can be found at https://webdav.tuebingen.mpg.de/pamy2/
Design, Control, and Evaluation of a Human-Inspired Robotic Eye
Schulz S. Design, Control, and Evaluation of a Human-Inspired Robotic Eye. Bielefeld: Universität Bielefeld; 2020.The field of human-robot interaction deals with robotic systems that involve
humans and robots closely interacting with each other. With these systems
getting more complex, users can be easily overburdened by the operation
and can fail to infer the internal state of the system or its ”intentions”. A
social robot, replicating the human eye region with its familiar features and
movement patterns, that are the result of years of evolution, can counter
this. However, the replication of these patterns requires hard- and software
that is able to compete with the human characteristics and performance.
Comparing previous systems found in literature with the human capabili-
ties reveal a mismatch in this regard. Even though individual systems solve
single aspects, the successful combination into a complete system remains
an open challenge. In contrast to previous work, this thesis targets to close
this gap by viewing the system as a whole — optimizing the hard- and
software, while focusing on the replication of the human model right from
the beginning. This work ultimately provides a set of interlocking building
blocks that, taken together, form a complete end-to-end solution for the de-
sign, control, and evaluation of a human-inspired robotic eye. Based on the
study of the human eye, the key driving factors are identified as the success-
ful combination of aesthetic appeal, sensory capabilities, performance, and
functionality. Two hardware prototypes, each based on a different actua-
tion scheme, have been developed in this context. Furthermore, both hard-
ware prototypes are evaluated against each other, a previous prototype, and
the human by comparing objective numbers obtained by real-world mea-
surements of the real hardware. In addition, a human-inspired and model-
driven control framework is developed out, again, following the predefined
criteria and requirements. The quality and human-likeness of the motion,
generated by this model, is evaluated by means of a user study. This frame-
work not only allows the replication of human-like motion on the specific
eye prototype presented in this thesis, but also promotes the porting and
adaption to less equipped humanoid robotic heads. Unlike previous systems
found in literature, the presented approach provides a scaling and limiting
function that allows intuitive adjustments of the control model, which can
be used to reduce the requirements set on the target platform. Even though
a reduction of the overall velocities and accelerations will result in a slower
motion execution, the human characteristics and the overall composition of
the interlocked motion patterns remain unchanged
Smooth Exploration for Robotic Reinforcement Learning
Reinforcement learning (RL) enables robots to learn skills from interactions
with the real world. In practice, the unstructured step-based exploration used
in Deep RL -- often very successful in simulation -- leads to jerky motion
patterns on real robots. Consequences of the resulting shaky behavior are poor
exploration, or even damage to the robot. We address these issues by adapting
state-dependent exploration (SDE) to current Deep RL algorithms. To enable this
adaptation, we propose two extensions to the original SDE, using more general
features and re-sampling the noise periodically, which leads to a new
exploration method generalized state-dependent exploration (gSDE). We evaluate
gSDE both in simulation, on PyBullet continuous control tasks, and directly on
three different real robots: a tendon-driven elastic robot, a quadruped and an
RC car. The noise sampling interval of gSDE permits to have a compromise
between performance and smoothness, which allows training directly on the real
robots without loss of performance. The code is available at
https://github.com/DLR-RM/stable-baselines3.Comment: Code: https://github.com/DLR-RM/stable-baselines3/ Training scripts:
https://github.com/DLR-RM/rl-baselines3-zoo
Ball-and-socket joint pose estimation using magnetic field
Roboy 3.0 is an open-source tendon-driven humanoid robot that mimics the
musculoskeletal system of the human body. Roboy 3.0 is being developed as a
remote robotic body - or a robotic avatar - for humans to achieve remote
physical presence. Artificial muscles and tendons allow it to closely resemble
human morphology with 3-DoF neck, shoulders and wrists. Roboy 3.0 3-DoF joints
are implemented as ball-and-socket joints. While industry provides a clear
solution for 1-DoF joint pose sensing, it is not the case for the
ball-and-socket joint type. In this paper we present a custom solution to
estimate the pose of a ball-and-socket joint. We embed an array of magnets into
the ball and an array of 3D magnetic sensors into the socket. We then, based on
the changes in the magnetic field as the joint rotates, are able to estimate
the orientation of the joint. We evaluate the performance of two neural network
approaches using the LSTM and Bayesian-filter like DVBF. Results show that in
order to achieve the same mean square error (MSE) DVBFs require significantly
more time training and hyperparameter tuning compared to LSTMs, while DVBF cope
with sensor noise better. Both methods are capable of real-time joint pose
estimation at 37 Hz with MSE of around 0.03 rad for all three degrees of
freedom combined. The LSTM model is deployed and used for joint pose estimation
of Roboy 3.0's shoulder and neck joints. The software implementation and PCB
designs are open-sourced under
https://github.com/Roboy/ball_and_socket_estimatorComment: Accepted at the International Symposium on Robotics Research (ISRR)
202
The design, analysis and evaluation of a humanoid robotic head
Where robots interact directly with humans on a ‘one-to-one’ basis, it is often quite important for them to be emotionally acceptable, hence the growing interesting in humanoid robots. In some applications it is important that these robots do not just resemble a human being in appearance, but also move like a human being too, to make them emotionally acceptable – hence the interest in biomimetic humanoid robotics. The research described in this thesis is concerned with the design, analysis and evaluation of a biomimetic humanoid robotic head. It is biomimetic in terms of physical design - which is based around a simulated cervical spine, and actuation, which is achieved using pneumatic air muscles (PAMS). The primary purpose of the research, however, and the main original contribution, was to create a humanoid robotic head capable of mimicking complex non-purely rotational human head movements. These include a sliding front-to-back, lateral movement, and a sliding, side-to-side lateral movement. A number of different approaches were considered and evaluated, before finalising the design.
As there are no generally accepted metrics in the literature regarding the full range of human head movements, the best benchmarks for comparison are the angular ranges and speeds of humans in terms on pitch (nod), roll (tilt) and yaw (rotate) were used for comparison, and these they were considered desired ranges for the robot. These measured up well in comparison in terms of angular speed and some aspects of range of human necks. Additionally, the lateral movements were measured during the nod, tilt and rotate movements, and established the ability of the robot to perform the complex lateral movements seen in humans, thus proving the benefits of the cervical spine approach.
Finally, the emotional acceptance of the robot movements was evaluated against another (commercially made) robot and a human. This was a blind test, in that the (human) evaluators had no way of knowing whether they were evaluation a human or a robot. The tests demonstrated that on scales of Fake/Natural, Machinelike/Humanlike and Unconcsious/Conscious the robot the robot scored similarly to the human
Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks
Objective: To test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Background: Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction nor generalisation of independently varying, active and passive states. We use deep learning to investigate the generalizable content of 2D US muscle images. Method: US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle were recorded from 32 healthy participants (7 female, ages: 27.5, 19-65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, driftfree, components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous, independent variation of passive (joint angle) and active (electromyography) inputs. Results: For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography, and joint moment were estimated to accuracy 55±8%, 57±11%, and 46±9% respectively. Significance: With 2D US imaging, deep neural networks can encode in generalizable form, the activitylength-tension state relationship of these muscles. Observation only, low power, 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalised muscle assessment in pain, injury, neurological conditions, neuropathies, myopathies and ageing
- …