467 research outputs found
Gaussian Processes for Machine Learning in Robotics
Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as
perception, control, planning, and decision making. Machine learning involves learning,
reasoning, and acting based on the data. This is achieved by constructing computer
programs that process the data, extract useful information or features, make predictions to
infer unknown properties, and suggest actions to take or decisions to make. This computer
program corresponds to a mathematical model of the data that describes the relationship
between the variables that represent the observed data and properties of interest. The
aforementioned model is learned based on the available training data, which is accomplished
using a learning algorithm capable of automatically adjusting the parameters of
the model to agree with the data. Therefore, the architecture of the model needs to be selected
accordingly, which is not a trivial task and usually depends on the machine-learning
engineer’s insights and past experience. The number of parameters to be tuned varies significantly
with the selected machine learning model, ranging from two or three parameters
for Gaussian processes (GP) to hundreds of thousands for artificial neural networks.
However, as more complex and novel robotic applications emerge, data complexity
increases and prior experience may be insufficient to define adequate mathematical models.
In addition, traditional machine learning methods are prone to problems such as
overfitting, which can lead to inaccurate predictions and catastrophic failures in critical
applications. These methods provide probabilistic distributions as model outputs, allowing
for estimating the uncertainty associated with predictions and making more informed
decisions. That is, they provide a mean and variance for the model responses.
This thesis focuses on the application of machine learning solutions based on Gaussian
processes to various problems in robotics, with the aim of improving current methods and
providing a new perspective. Key areas such as trajectory planning for unmanned aerial
vehicles (UAVs), motion planning for robotic manipulators and model identification of
nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that
allow for more efficient planning and energy savings in exploration missions have been
developed. These algorithms are compared with traditional analytical approaches, demonstrating
their superiority in terms of efficiency when using machine learning. Area coverage
and linear coverage algorithms with UAV formations are presented, as well as a
sea surface search algorithm. Finally, these algorithms are compared with a new method
that uses Gaussian processes to perform probabilistic predictions and optimise trajectory
planning, resulting in improved performance and reduced energy consumption.
Regarding motion planning for robotic manipulators, an approach based on Gaussian
process models that provides a significant reduction in computational times is proposed.
A Gaussian process model is used to approximate the configuration space of a robot,
which provides valuable information to avoid collisions and improve safety in dynamic
environments. This approach is compared to conventional collision checking methods
and its effectiveness in terms of computational time and accuracy is demonstrated. In this
application, the variance provides information about dangerous zones for the manipulator.
In terms of creating models of non-linear systems, Gaussian processes also offer significant
advantages. This approach is applied to a soft robotic arm system and UAV energy
consumption models, where experimental data is used to train Gaussian process models
that capture the relationships between system inputs and outputs. The results show accurate
identification of system parameters and the ability to make reliable future predictions.
In summary, this thesis presents a variety of applications of Gaussian processes in
robotics, from trajectory and motion planning to model identification. These machine
learning-based solutions provide probabilistic predictions and improve the ability of robots
to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful
tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia
gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de
decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar
datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o
sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan
las relaciones entre las variables que representan los datos observados y las propiedades
que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización
que ajustan automáticamente los parámetros para lograr un rendimiento óptimo.
Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas,
la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente
para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático
tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar
a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos
desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos
gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas
como salidas del modelo, lo que permite estimar la incertidumbre asociada a las
predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una
varianza para las respuestas del modelo.
Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas
en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los
métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la
planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación
de movimientos para manipuladores robóticos y la identificación de modelos de sistemas
no lineales.
En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y
un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los
enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia
al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas
y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda
en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método
que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la
planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción
del consumo de energía.
En cuanto a la planificación de movimientos para manipuladores robóticos, se propone
un enfoque basado en modelos gaussianos que permite una reducción significativa
en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar
el espacio de configuraciones de un robot, lo que proporciona información valiosa para
evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara
con los métodos convencionales de planificación de movimientos y se demuestra su eficacia
en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación,
la varianza proporciona información sobre zonas peligrosas para el manipulador.
En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos
también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de
brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan
datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones
entre las entradas y las salidas del sistema. Los resultados muestran una identificación
precisa de los parámetros del sistema y la capacidad de realizar predicciones
futuras confiables.
En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos
en robótica, desde la planificación de trayectorias y movimientos hasta la identificación
de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones
probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura
y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para
abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon
Graph-based 3D Collision-distance Estimation Network with Probabilistic Graph Rewiring
We aim to solve the problem of data-driven collision-distance estimation
given 3-dimensional (3D) geometries. Conventional algorithms suffer from low
accuracy due to their reliance on limited representations, such as point
clouds. In contrast, our previous graph-based model, GraphDistNet, achieves
high accuracy using edge information but incurs higher message-passing costs
with growing graph size, limiting its applicability to 3D geometries. To
overcome these challenges, we propose GDN-R, a novel 3D graph-based estimation
network.GDN-R employs a layer-wise probabilistic graph-rewiring algorithm
leveraging the differentiable Gumbel-top-K relaxation. Our method accurately
infers minimum distances through iterative graph rewiring and updating relevant
embeddings. The probabilistic rewiring enables fast and robust embedding with
respect to unforeseen categories of geometries. Through 41,412 random benchmark
tasks with 150 pairs of 3D objects, we show GDN-R outperforms state-of-the-art
baseline methods in terms of accuracy and generalizability. We also show that
the proposed rewiring improves the update performance reducing the size of the
estimation model. We finally show its batch prediction and auto-differentiation
capabilities for trajectory optimization in both simulated and real-world
scenarios.Comment: 7 pages, 6 figure
Application of big data in transportation safety analysis using statistical and deep learning methods
The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated
GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP
Full detector simulation was among the largest CPU consumer in all CERN
experiment software stacks for the first two runs of the Large Hadron Collider
(LHC). In the early 2010's, the projections were that simulation demands would
scale linearly with luminosity increase, compensated only partially by an
increase of computing resources. The extension of fast simulation approaches to
more use cases, covering a larger fraction of the simulation budget, is only
part of the solution due to intrinsic precision limitations. The remainder
corresponds to speeding-up the simulation software by several factors, which is
out of reach using simple optimizations on the current code base. In this
context, the GeantV R&D project was launched, aiming to redesign the legacy
particle transport codes in order to make them benefit from fine-grained
parallelism features such as vectorization, but also from increased code and
data locality. This paper presents extensively the results and achievements of
this R&D, as well as the conclusions and lessons learnt from the beta
prototype.Comment: 34 pages, 26 figures, 24 table
Enhanced online programming for industrial robots
The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal
- …