A survey of free software for the design, analysis, modelling, and simulation of an unmanned aerial vehicle

The objective of this paper is to analyze free software for the design, analysis, modelling, and simulation of an unmanned aerial vehicle (UAV). Free software is the best choice when the reduction of production costs is necessary; nevertheless, the quality of free software may vary. This paper probably does not include all of the free software, but tries to describe or mention at least the most interesting programs. The first part of this paper summarizes the essential knowledge about UAVs, including the fundamentals of flight mechanics and aerodynamics, and the structure of a UAV system. The second section generally explains the modelling and simulation of a UAV. In the main section, more than 50 free programs for the design, analysis, modelling, and simulation of a UAV are described. Although the selection of the free software has been focused on small subsonic UAVs, the software can also be used for other categories of aircraft in some cases; e.g. for MAVs and large gliders. The applications with an historical importance are also included. Finally, the results of the analysis are evaluated and discussed—a block diagram of the free software is presented, possible connections between the programs are outlined, and future improvements of the free software are suggested. © 2015, CIMNE, Barcelona, Spain.Internal Grant Agency of Tomas Bata University in Zlin [IGA/FAI/2015/001, IGA/FAI/2014/006

Geometric-based Optimization Algorithms for Cable Routing and Branching in Cluttered Environments

The need for designing lighter and more compact systems often leaves limited space for planning routes for the connectors that enable interactions among the system’s components. Finding optimal routes for these connectors in a densely populated environment left behind at the detail design stage has been a challenging problem for decades. A variety of deterministic as well as heuristic methods has been developed to address different instances of this problem. While the focus of the deterministic methods is primarily on the optimality of the final solution, the heuristics offer acceptable solutions, especially for such problems, in a reasonable amount of time without guaranteeing to find optimal solutions. This study is an attempt to furthering the efforts in deterministic optimization methods to tackle the routing problem in two and three dimensions by focusing on the optimality of final solutions. The objective of this research is twofold. First, a mathematical framework is proposed for the optimization of the layout of wiring connectors in planar cluttered environments. The problem looks at finding the optimal tree network that spans multiple components to be connected with the aim of minimizing the overall length of the connectors while maximizing their common length (for maintainability and traceability of connectors). The optimization problem is formulated as a bi-objective problem and two solution methods are proposed: (1) to solve for the optimal locations of a known number of breakouts (where the connectors branch out) using mixed-binary optimization and visibility notion and (2) to find the minimum length tree that spans multiple components of the system and generates the optimal layout using the previously-developed convex hull based routing. The computational performance of these methods in solving a variety of problems is further evaluated. Second, the problem of finding the shortest route connecting two given nodes in a 3D cluttered environment is considered and addressed through deterministically generating a graphical representation of the collision-free space and searching for the shortest path on the found graph. The method is tested on sample workspaces with scattered convex polyhedra and its computational performance is evaluated. The work demonstrates the NP-hardness aspect of the problem which becomes quickly intractable as added components or increase in facets are considered

On Fast Large-Scale Program Analysis in Datalog

Designing and crafting a static program analysis is challenging due to the complexity of the task at hand. Among the challenges are modelling the semantics of the input language, finding suitable abstractions for the analysis, and handwriting efficient code for the analysis in a traditional imperative language such as C++. Hence, the development of static program analysis tools is costly in terms of development time and resources for real world languages. To overcome, or at least alleviate the costs of developing a static program analysis, Datalog has been proposed as a domain specific language (DSL).With Datalog, a designer expresses a static program analysis in the form of a logical specification. While a domain specific language approach aids in the ease of development of program analyses, it is commonly accepted that such an approach has worse runtime performance than handcrafted static analysis tools. In this work, we introduce a new program synthesis methodology for Datalog specifications to produce highly efficient monolithic C++ analyzers. The synthesis technique requires the re-interpretation of the semi-naïve evaluation as a scaffolding for translation using partial evaluation. To achieve high-performance, we employ staged compilation techniques and specialize the underlying relational data structures for a given Datalog specification. Experimentation on benchmarks for large-scale program analysis validates the superior performance of our approach over available Datalog tools and demonstrates our competitiveness with state-of-the-art handcrafted tools

A heuristic algorithm for determining the part set in a powder-bed additive manufacturing machine.

The goal of this research is to develop a procedure for the placement of a priority part into a planned build in a powder-bed additive manufacturing machine. Toward that goal, a heuristic procedure was developed that seeks to maximize the revenue in a scheduled build, subject to due-date constraints; i.e., all parts with the closest due date will not only be included in the build, but will be placed near the bottom of the build. Likewise, any part in the scheduled build that does not have an immediate due date is a candidate for removal in order to accommodate a higher priority part, the order for which arrives after the build is underway. The build volume for this experiment with the SLS2500+ machine is 13-inch x 15-inch x 18-inch rectilinear container. To achieve this optimization of the build, the experiment for this research involved a penalty which is based on failure to meet the due date constraint. The m parts in the build are placed such that are placed the highest priority parts, including those with immediate due date constraints, are positioned near the bottom of the build volume. The focus of this study is to determine which “unbuilt” parts x(i) will be removed to make room for higher priority parts. Toward that end, each part is assigned a priority p(i), i = 1, …,m until the build is full. If an order for a part having a higher priority p(i)than any of the parts in the build, the procedure developed here seeks to replace at least one of the parts in the scheduled build. Hence, the dimensions of the lower priority parts are key, since the part that is removed must provide sufficient space for the new part. Importantly, the enactment of the procedure developed here takes place in real time, with little or no interruption of the progress of the build. The resulting Excel file will be sorted as to place the immediate items at the bottom on the build. The solution is valid for a single and also for a multiple build. Simulations of this heuristic procedure were shown to substantially increase the revenue desired from the planned build by adding higher priority parts

A Study of Speed of the Boundary Element Method as applied to the Realtime Computational Simulation of Biological Organs

In this work, possibility of simulating biological organs in realtime using the Boundary Element Method (BEM) is investigated. Biological organs are assumed to follow linear elastostatic material behavior, and constant boundary element is the element type used. First, a Graphics Processing Unit (GPU) is used to speed up the BEM computations to achieve the realtime performance. Next, instead of the GPU, a computer cluster is used. Results indicate that BEM is fast enough to provide for realtime graphics if biological organs are assumed to follow linear elastostatic material behavior. Although the present work does not conduct any simulation using nonlinear material models, results from using the linear elastostatic material model imply that it would be difficult to obtain realtime performance if highly nonlinear material models that properly characterize biological organs are used. Although the use of BEM for the simulation of biological organs is not new, the results presented in the present study are not found elsewhere in the literature.Comment: preprint, draft, 2 tables, 47 references, 7 files, Codes that can solve three dimensional linear elastostatic problems using constant boundary elements (of triangular shape) while ignoring body forces are provided as supplementary files; codes are distributed under the MIT License in three versions: i) MATLAB version ii) Fortran 90 version (sequential code) iii) Fortran 90 version (parallel code

A Framework for Evolutionary Optimization Applications in Water Distribution Systems

The application of optimization to Water Distribution Systems encompasses the use of computer-based techniques to problems of many different areas of system design, maintenance and operational management. As well as laying out the configuration of new WDS networks, optimization is commonly needed to assist in the rehabilitation or reinforcement of existing network infrastructure in which alternative scenarios driven by investment constraints and hydraulic performance are used to demonstrate a cost-benefit relationship between different network intervention strategies. Moreover, the ongoing operation of a WDS is also subject to optimization, particularly with respect to the minimization of energy costs associated with pumping and storage and the calibration of hydraulic network models to match observed field data. Increasingly, Evolutionary Optimization techniques, of which Genetic Algorithms are the best-known examples, are applied to aid practitioners in these facets of design, management and operation of water distribution networks as part of Decision Support Systems (DSS). Evolutionary Optimization employs processes akin to those of natural selection and “survival of the fittest” to manipulate a population of individual solutions, which, over time, “evolve” towards optimal solutions. Such algorithms are characterized, however, by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of water networks incurs significant computational overheads, can limit the applicability and scalability of this technology in this domain. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to Water Distribution Systems. A number of new procedures are presented for improving the performance of such algorithms when applied to complex engineering problems. These techniques approach the problem of minimising the impact of the inherent computational complexity of these problems from a number of angles. A novel genetic representation is presented which combines the algorithmic simplicity of the classical binary string of the Genetic Algorithm with the performance advantages inherent in an integer-based representation. Further algorithmic improvements are demonstrated with an intelligent mutation operator that “learns” which genes have the greatest impact on the quality of a solution and concentrates the mutation operations on those genes. A technique for implementing caching of solutions – recalling the results for solutions that have already been calculated - is demonstrated to reduce runtimes for Genetic Algorithms where applied to problems with significant computation complexity in their evaluation functions. A novel reformulation of the Genetic Algorithm for implementing robust stochastic optimizations is presented which employs the caching technology developed to produce an multiple-objective optimization methodology that demonstrates dramatically improved quality of solutions for given runtime of the algorithm. These extensions to the Genetic Algorithm techniques are coupled with a supporting software library that represents a standardized modelling architecture for the representation of connected networks. This library gives rise to a system for distributing the computational load of hydraulic simulations across a network of computers. This methodology is established to provide a viable, scalable technique for accelerating evolutionary optimization applications.Engineering and Physical Sciences Research Council, UK

Easing parallel programming on heterogeneous systems

El modo más frecuente de resolver aplicaciones de HPC (High performance Computing) en tiempos de ejecución razonables y de una forma escalable es mediante el uso de sistemas de cómputo paralelo. La tendencia actual en los sistemas de HPC es la inclusión en la misma máquina de ejecución de varios dispositivos de cómputo, de diferente tipo y arquitectura. Sin embargo, su uso impone al programador retos específicos. Un programador debe ser experto en las herramientas y abstracciones existentes para memoria distribuida, los modelos de programación para sistemas de memoria compartida, y los modelos de programación específicos para para cada tipo de co-procesador, con el fin de crear programas híbridos que puedan explotar eficientemente todas las capacidades de la máquina. Actualmente, todos estos problemas deben ser resueltos por el programador, haciendo así la programación de una máquina heterogénea un auténtico reto. Esta Tesis trata varios de los problemas principales relacionados con la programación en paralelo de los sistemas altamente heterogéneos y distribuidos. En ella se realizan propuestas que resuelven problemas que van desde la creación de códigos portables entre diferentes tipos de dispositivos, aceleradores, y arquitecturas, consiguiendo a su vez máxima eficiencia, hasta los problemas que aparecen en los sistemas de memoria distribuida relacionados con las comunicaciones y la partición de estructuras de datosDepartamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

Constrained Motion Planning System for MRI-Guided, Needle-Based, Robotic Interventions

In needle-based surgical interventions, accurate alignment and insertion of the tool is paramount for providing proper treatment at a target site while minimizing healthy tissue damage. While manually-aligned interventions are well-established, robotics platforms promise to reduce procedure time, increase precision, and improve patient comfort and survival rates. Conducting interventions in an MRI scanner can provide real-time, closed-loop feedback for a robotics platform, improving its accuracy, yet the tight environment potentially impairs motion, and perceiving this limitation when planning a procedure can be challenging. This project developed a surgical workflow and software system for evaluating the workspace and planning the motions of a robotics platform within the confines of an MRI scanner. 3D Slicer, a medical imaging visualization and processing platform, provided a familiar and intuitive interface for operators to quickly plan procedures with the robotics platform over OpenIGTLink. Robotics tools such as ROS and MoveIt! were utilized to analyze the workspace of the robot within the patient and formulate the motion planning solution for positioning of the robot during surgical procedures. For this study, a 7 DOF robot arm designed for ultrasonic ablation of brain tumors was the targeted platform. The realized system successfully yielded prototype capabilities on the neurobot for conducting workspace analysis and motion planning, integrated systems using OpenIGTLink, provided an opportunity to evaluate current software packages, and informed future work towards production-grade medical software for MRI-guided, needle-based robotic interventions