283 research outputs found
Simplified and Smoothed Rapidly-Exploring Random Tree Algorithm for Robot Path Planning
Rapidly-exploring Random Tree (RRT) is a prominent algorithm with quite successful results in achieving the optimal solution used to solve robot path planning problems. The RRT algorithm works by creating iteratively progressing random waypoints from the initial waypoint to the goal waypoint. The critical problem in the robot movement is the movement and time costs caused by the excessive number of waypoints required to be able to reach the goal, which is why reducing the number of waypoints created after path planning is an important process in solving the robot path problem. Ramer-Douglas-Peucker (RDP) is an effective algorithm to reduce waypoints. In this study, the Waypoint Simplified and Smoothed RRT Method (WSS-RRT) is proposed which reduces the distance costs between 8.13% and 13.36% by using the RDP algorithm to reduce the path into the same path with fewer waypoints, which is an array of waypoints created by the RRT algorithm
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
OptimizaciĂłn de una metodologĂa para el uso de herramientas de diseño estructural paramĂ©trico
La metodologĂa propuesta busca establecer una forma de diseño de estructuras paramĂ©tricas mediante
la interacciĂłn de la geometrĂa del modelo de diseño. Se llevará a cabo una investigaciĂłn utilizando
casos de estudio en el software Rhinoceros 3D con el complemento de Grasshopper/Karamba 3D
para el análisis estructural. Los objetivos son analizar el uso y proceso de modelado en Rhinoceros
y Grasshopper, aplicar el diseño paramĂ©trico en los casos de estudio, optimizar una metodologĂa
basada en el análisis de casos, y promover el uso de herramientas paramétricas en el análisis y diseño
estructural desde las etapas iniciales de un proyecto arquitectónico. Las estructuras paramétricas
en Karamba 3D tienen aplicaciones prácticas en la arquitectura al permitir controlar el diseño de
manera eficiente y responder al entorno. La integraciĂłn con Grasshopper mejora la exploraciĂłn
del espacio de diseño y agiliza el procesamiento de datos complejos. El diseño paramétrico utiliza
algoritmos y ecuaciones matemáticas para generar formas basadas en proporciones matemáticas
y armoniosas. El uso de herramientas como Karamba 3D y Galápagos permite realizar análisis
estructurales paramétricos detallados, considerando cargas, materiales y normas de diseño. Estas
herramientas ofrecen ventajas al permitir explorar diversas configuraciones estructurales y optimizar
el diseño segĂşn criterios de desempeño especĂficos. Se aplicaron análisis estructurales paramĂ©tricos
en casos especĂficos, como el Museo Twist, el Restaurante de los Manantiales y la Casa Mirador,
para optimizar momentos, desplazamientos, esfuerzos, forma y peso. Estos análisis mejoraron la
eficiencia y seguridad de las estructuras mediante ajustes paramétricos y optimizaciones.The proposed methodology seeks to establish a design form of parametric structures through the
interaction of the geometry of the design model. The research will be conducted using case studies in
the Rhinoceros 3D software with the Grasshopper/Karamba 3D complement for structural analysis.
The objectives are to analyze the use and process of modeling in Rhinoceros and Grasshopper,
apply the parametric design in the case studies, optimize a methodology based on case analysis,
and promote the use of parametric tools in the analysis and structural design from the initial stages
of an architectural project. The parametric structures in Karamba 3D have practical applications
in architecture by allowing them to control the design efficiently and respond to the environment.
Integration with Grasshopper improves design space exploration and streamlines complex data
processing. The parametric design uses mathematical algorithms and equations to generate shapes
based on mathematical and harmonious proportions. The use of tools such as Karamba 3D and
Galapagos allows detailed parametric structural analysis, considering loads, materials, and design
standards. These tools offer advantages by allowing you to explore various structural configurations
and optimize the design according to specific performance criteria. Parametric structural analyses
were applied in specific cases, such as the Twist Museum, the Restaurant of the Springs, and the Casa
Mirador, to optimize moments, displacements, efforts, shape, and weight. These analyses improved
the efficiency and safety of the structures through parametric adjustments and optimizations.0000-0003-1841-412
Airfoil Optimization using Design-by-Morphing
We present Design-by-Morphing (DbM), a novel design methodology applicable to
creating a search space for topology optimization of 2D airfoils. Most design
techniques impose geometric constraints and sometimes designers' bias on the
design space itself, thus restricting the novelty of the designs created, and
only allowing for small local changes. We show that DbM methodology does not
impose any such restrictions on the design space and allows for extrapolation
from the search space, thus granting truly radical and large search space with
a few design parameters. In comparison to other shape design methodologies, we
apply DbM to create a search space for 2D airfoils. We optimize this airfoil
shape design space for maximizing the lift-over-drag ratio, , and
stall angle tolerance, . Using a bi-objective genetic algorithm
to optimize the DbM space, it is found that we create a Pareto-front of radical
airfoils exhibiting remarkable properties for both objectives
Suitability of dynamic environments in virtual reality for schizophrenia therapies
Serious Games (SG) have a significant positive
impact on a wide range of purposes. Serious virtual games focused
on the rehabilitation of schizophrenia are able to bring
improvements to patients who require constant and individualized
rehabilitation. Schizophrenia is a mental illness that has no cure
and requires intensive rehabilitation for symptoms relief. Virtual
Reality (VR) has gained acceptance in the medical field for a
variety of rehabilitation by virtue of its immersiveness and
versatility. Virtual environments can assist this process, seeking to
contemplate important topics for their daily lives, facilitating the
understanding of their situation and difficulties. To achieve more
effective results, a virtual scenario must be dynamic and suitable
to each patient. Nonplayable Character (NPC), which in this work
can be human-like or pet-like representations, tasks, varied and
objective interactions, and the ambiance all play an important role
in determining the expected result. The work described in this
paper approaches previously mentioned topics by investigating
the contribution of previous works in this context for approaching
multiple elements that control the dynamic environment, bringing
new modalities and research for the development of a SG for VR
focused on the rehabilitation of schizophrenia by facilitating
schizophrenics’ daily lives.This work is funded by the European Regional Development
Fund (ERDF) through the Regional Operational Program North
2020, within the scope of Project GreenHealth - Digital
strategies in biological assets to improve well-being and
promote Green health, Norte-01-0145-FEDER-000042.info:eu-repo/semantics/publishedVersio
The non-orthogonal Serret–Frenet parametrization applied to the path following problem of a manipulator with partially known dynamics
In this paper an application of the Serret–Frenet parametrization of a curve to the path following task is presented. This curvilinear parametrization method is used to obtain a control object description relative to the desired curve defined in the three-dimensional space. In order to derive proper equations, the innovative approach of the non-orthogonal projection of a control object on the given path is investigated. The non-orthogonal projection allows to design a global control algorithm. The proposed solution results in a cascade structure of the control system. Thus, the backstepping integrator algorithm was applied to create a control law. Due to the partial knowledge of control object dynamic parameters, an adaptive algorithm is taken into account. Theoretical considerations are confirmed with simulation study. Conducted simulations illustrated following paths at different levels of complexity by a holonomic non-redundant manipulator with a fixed base
On the motion planning & control of nonlinear robotic systems
In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance
Vectorizing binary image boundaries with symmetric shape detection, bisection and optimal parameterization
Binary image boundary vectorization is the process of converting raster images into vector images represented with a sequence of BĂ©zier curves. Two main factors in reconstructing parametric curves are to approximate the underlying structure of the boundaries as much as possible while using as few curves as possible. Existing methods do not perform well when considering both of these two main factors. In this article, we mimic the process of human vectorizing image boundaries by first segmenting the boundary points into multiple segments with the corner points. For the boundary points in each segment, we adopt the bisection method to find the largest number of points, which a single curve can fit. More curves will be added if the fitting error is larger than a predefined threshold. The process is repeated until all the points in the segment are fitted, thus minimizing the number of BĂ©zier curves. Besides, symmetric image boundaries can be detected and used to further decrease the number of curves required. Our method can also choose the optimal parameterization method case by case to further reduce the fitting error. We make a comparison with both new and classical methods and show that our method outperforms them
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
Even though trained mainly on images, we discover that pretrained diffusion
models show impressive power in guiding sketch synthesis. In this paper, we
present DiffSketcher, an innovative algorithm that creates vectorized free-hand
sketches using natural language input. DiffSketcher is developed based on a
pre-trained text-to-image diffusion model. It performs the task by directly
optimizing a set of Bezier curves with an extended version of the score
distillation sampling (SDS) loss, which allows us to use a raster-level
diffusion model as a prior for optimizing a parametric vectorized sketch
generator. Furthermore, we explore attention maps embedded in the diffusion
model for effective stroke initialization to speed up the generation process.
The generated sketches demonstrate multiple levels of abstraction while
maintaining recognizability, underlying structure, and essential visual details
of the subject drawn. Our experiments show that DiffSketcher achieves greater
quality than prior work.Comment: 14 pages, 8 figures. update: improved experiment analysis, fixed
typos, and fixed image error
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