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Particulate systems in practical applications such as biomass combustion, blood cellular systems and granular particles in fluidized beds, have often been computationally represented using spherical surfaces, even though the majority of particles in archetypal fluid-solid systems are non-spherical. While spherical particles are more cost-effective to simulate, notable deficiencies of these implementations are their substantial inaccuracies in predicting the dynamics of particle mixtures. Alternatively, modeling dense fluid-particulate systems using non-spherical particles involves increased complexity, with computational cost manifesting as the biggest bottleneck. However, with recent advancements in computer hardware, simulations of three-dimensional particulate systems using irregular shaped particles have garnered significant interest. In this research, a novel Discrete Element Method (DEM) model that incorporates geometry definition, collision detection, and post-collision kinematics has been developed to accurately simulate non-spherical particulate systems. Superellipsoids, which account for 80% of particles commonly found in nature, are used to represent non-spherical shapes. Collisions between these particles are processed using a distance function computation carried out with respect to their surfaces. An event - driven model and a time-driven model have been employed in the current framework to resolve collisions. The collision model's influence on non–spherical particle dynamics is verified by observing the conservation of momentum and total kinetic energy. Furthermore, the non-spherical DEM model is coupled with an in-house fluid flow solver (GenIDLEST). The combined CFD-DEM model's results are validated by comparing to experimental measurements in a fluidized bed. The parallel scalability of the non-spherical DEM model is evaluated in terms of its efficiency and speedup. Major factors affecting wall clock time of simulations are analyzed and an estimate of the model's dependency on these factors is documented. The developed framework allows for a wide range of particle geometries to be simulated in GenIDLEST. / Master of Science / CFD – DEM (Discrete Element Method) is a technique of coupling fluid flow solvers with granular solid particles. CFD – DEM simulations are beneficial in recreating pragmatic applications such as blood cellular flows, fluidized beds and pharmaceutics. Up until recently, particles in these flows have been modeled as spheres as the generation of particle geometry and collision detection algorithms are straightforward. However, in real – life occurrences, most particles are irregular in shape, and approximating them as spheres in computational works leads to a substantial loss of accuracy. On the other hand, non – spherical particles are more complex to generate. When these particles are in motion, they collide and exhibit complex trajectories. Majority of the wall clock time is spent in resolving collisions between these non – spherical particles. Hence, generic algorithms to detect and resolve collisions have to be incorporated. This primary focus of this research work is to develop collision detection and resolution algorithms for non – spherical particles. Collisions are detected using inherent geometrical properties of the class of particles used. Two popular models (event-driven and time-driven) are implemented and utilized to update the trajectories of particles. These models are coupled with an in – house fluid solver (GenIDLEST) and the functioning of the DEM model is validated with experimental results from previous research works. Also, since the computational effort required is higher in the case of non – spherical particulate simulations, an estimate of the scalability of the problem and factors influencing time to simulations are presented.

This thesis focuses on applying one of the rapidly growing non-deterministic optimization algorithms, the ant colony algorithm, for simulating automatic hose/pipe routing with several conflicting objectives. Within the thesis, methods have been developed and applied to single objective hose routing, multi-objective hose routing and multi-hose routing. The use of simulation and optimization in engineering design has been widely applied in all fields of engineering as the computational capabilities of computers has increased and improved. As a result of this, the application of non-deterministic optimization techniques such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc. has increased dramatically resulting in vast improvements in the design process. Initially, two versions of ant colony algorithms have been developed based on, respectively, a random network and a grid network for a single objective (minimizing the length of the hoses) and avoiding obstacles in the CAD model. While applying ant colony algorithms for the simulation of hose routing, two modifications have been proposed for reducing the size of the search space and avoiding the stagnation problem. Hose routing problems often consist of several conflicting or trade-off objectives. In classical approaches, in many cases, multiple objectives are aggregated into one single objective function and optimization is then treated as a single-objective optimization problem. In this thesis two versions of ant colony algorithms are presented for multihose routing with two conflicting objectives: minimizing the total length of the hoses and maximizing the total shared length (bundle length). In this case the two objectives are aggregated into a single objective. The current state-of-the-art approach for handling multi-objective design problems is to employ the concept of Pareto optimality. Within this thesis a new Pareto-based general purpose ant colony algorithm (PSACO) is proposed and applied to a multi-objective hose routing problem that consists of the following objectives: total length of the hoses between the start and the end locations, number of bends, and angles of bends. The proposed method is capable of handling any number of objectives and uses a single pheromone matrix for all the objectives. The domination concept is used for updating the pheromone matrix. Among the currently available multi-objective ant colony optimization (MOACO) algorithms, P-ACO generates very good solutions in the central part of the Pareto front and hence the proposed algorithm is compared with P-ACO. A new term is added to the random proportional rule of both of the algorithms (PSACO and P-ACO) to attract ants towards edges that make angles close to the pre-specified angles of bends. A refinement algorithm is also suggested for searching an acceptable solution after the completion of searching the entire search space. For all of the simulations, the STL format (tessellated format) for the obstacles is used in the algorithm instead of the original shapes of the obstacles. This STL format is passed to the C++ library RAPID for collision detection. As a result of using this format, the algorithms can handle freeform obstacles and the algorithms are not restricted to a particular software package.

The minimally invasive approach of arthroscopy means less pain and faster recovery time for patients compared to open surgery. However, it implies a high difficulty of performance. Therefore, surgeon should remain at a high level of technical and professional expertise to perform such operations. Surgeon’s skills are being developed over years of surgical training on animals, cadavers and patients. Nowadays, cadavers and animal specimens present an ethical problem also the practice on real humans is usually risky. For surgeons to reach a high level, new and alternative ways of performing surgical training are required. Virtual reality technology has opened new realms in the practice of medicine. Today, virtual reality simulators have become one of the most important training methods in the medical field. These simulators allow medical students to examine and study organs or any structure of the human body in ways that were not possible few years earlier. Similarly, the surgeon as well as the medical student can gain a valuable experience by performing a particular surgery with an anatomical accuracy and realism as it is actually performed in the real world. Thus, they can practice on virtual operation before they proceed and operate on real patients. In this thesis, a virtual reality training simulator for wrist arthroscopy is introduced. Two main issues are addressed: the 3-D reconstruction process and the 3-D interaction. Based on a sequence of CT images a realistic representation of the wrist joint is obtained suitable for the computer simulation. Two main components of the computer-based system interface are illustrated: the 3-D interaction to guide the surgical instruments and the user interface for haptic feedback. In this context, algorithms that model objects using the convex hull approaches and simulate real time exact collision detection between virtual objects are presented. A force feedback device, coupled with a haptic algorithm, is used as a haptic interface with the computer simulation system. This leads in the development of a low cost system with the same benefits as professional devices. In this regard, the wrist arthroscopy can be simulated and medical students can learn the basic skills required with safety, flexibility and less cost / La chirurgie arthroscopique présente actuellement un essor très important pour le bénéfice du plus grand nombre des patients. Cependant, cette technique possède un certain nombre d’inconvénients et il est donc nécessaire pour le médecin de s’entrainer et répéter ses gestes afin de pouvoir exécuter ce type d’opération d’une façon efficace et certaine. En effet, les méthodes traditionnelles d’enseignement de la chirurgie sont basées sur l’autopsie des cadavres et l’entrainement sur des animaux. Avec l’évolution de notre société, ces deux pratiques deviennent de plus en plus critiquées et font l’objet de réglementations très restrictives. Afin d’atteindre un niveau plus élevé, de nouveaux moyens d’apprentissage sont nécessaires pour les chirurgiens. Récemment, la réalité virtuelle commence d’être de plus en plus utilisée dans la médecine et surtout la chirurgie. Les simulateurs chirurgicaux sont devenus une des matières les plus récentes dans la recherche de la réalité virtuelle. Ils sont également devenus une méthode de formation et un outil d’entrainement valable pour les chirurgiens aussi bien que les étudiants en médecine. Dans ce travail, un simulateur de réalité virtuelle pour l’enseignement de la chirurgie arthroscopique, surtout la chirurgie du poignet, a été préesenté. Deux questions principales sont abordées : la reconstruction et l’interaction 3-D. Une séquence d’images CT a été traitée afin de générer un modèle 3-D du poignet. Les deux principales composantes de l’interface du système sont illustrées : l’interaction 3-D pour guider les instruments chirurgicaux et l’interface de l’utilisateur pour le retour d’effort. Dans ce contexte, les algorithmes qui modélisent les objets en utilisant les approches de “Convex Hull” et qui simulent la détection de collision entre les objets virtuels en temps réel, sont présentés. En outre, un dispositif de retour d’effort est utilisé comme une interface haptique avec le système. Cela conduit au développement d’un système à faible coût, avec les mêmes avantages que les appareils professionnels. A cet égard, l’arthroscopie du poignet peut être simulée et les étudiants en médecine peuvent facilement utiliser le système et peuvent apprendre les compétences de base requises en sécurité, flexibilité et moindre coût

En réponse au nombre toujours élevé de décès provoqués par les accidents routiers, l'industrie automobile a fait de la sécurité un sujet majeur de son activité global. Les radars automobiles qui étaient de simples capteurs pour véhicule de confort, sont devenus des éléments essentiels de la norme de sécurité routière. Le domaine de l’automobile est un domaine très exigent en terme de sécurité et les radars automobiles doivent avoir des performances de détection très élevées et doivent répondre à des nombreuses contraintes telles que la facilité de production et/ou le faible coût. Cette thèse concerne le développement d’algorithmes pour la détection et la localisation de piétons et de cyclistes pour des radars automobiles de nouvelle génération. Nous avons proposé une architecture de réseau d'antennes non uniforme optimale et des méthodes d'estimation spectrale à haute résolution permettant d’estimer avec précision la position angulaire des objets à partir de la direction d'arrivée (DoA) de leur réponse. Ces techniques sont adaptées à l'architecture du réseau d'antennes proposé et les performances sont évaluées à l'aide de données radar automobiles simulées et réelles acquises dans le cadre de scénarios spécifiques. Nous avons également proposé un détecteur de cible de collision, basé sur la décomposition en sous-espaces Doppler, dont l'objectif principal est d'identifier des cibles latérales dont les caractéristiques de trajectoire représentent potentiellement un danger de collision. Une méthode de calcul d'attribut de cible est également développée et un algorithme de classification est proposé pour discriminer les piétons, cyclistes et véhicules. Les différents algorithmes sont évalués et validés à l'aide de données radar automobiles réelles sur plusieurs scenarios. / In response to the persistently high number of deaths provoked by road crashes, the automotive industry has promoted safety as a major topic in their global activity. Automotive radars have been transformed from being simple sensors for comfort vehicle, to becoming essential elements of safety standard. The design of new generations automotive radars has to face various constraints and generally proposes a compromise between reliability, robustness, manufacturability, high-performance and low cost. The main objective of this PhD thesis is to design algorithms for the detection and localization of pedestrians and cyclists using new generation automotive radars. We propose an optimal non-uniform antenna array architecture and some high resolution spectral estimation methods to accurately estimate the position of objects from the direction of arrival (DOA) of their responses to the radar. These techniques are adapted to the proposed antenna array architecture and the performance is evaluated using both simulated and real automotive radar data, acquired in the frame of specific scenarios. We propose a collision target detector, based on the orthogonality of angle-Doppler subspaces, whose main goal is to identify lateral targets, whose trajectory features represent potentially a danger of collision. A target attribute calculation method is also developed and classification algorithm is proposed to classify pedestrian, cyclists and vehicles. This classification algorithm is evaluated and validated using real automotive radar data with several scenarios.

Purpose – The purpose of the study is to expand the knowledge of how wheel motor currents can be combined with machine learning to be used in a collision detection system for autonomous robots, in order to decrease the number of external sensors and open new design opportunities and lowering production costs. Method – The study is conducted with design science research where two artefacts are developed in a cooperation with Globe Tools Group. The artefacts are evaluated in how they categorize data given by an autonomous robot in the two categories collision and non-collision. The artefacts are then tested by generated data to analyse their ability to categorize. Findings – Both artefacts showed a 100 % accuracy in detecting the collisions in the given data by the autonomous robot. In the second part of the experiment the artefacts show that they have different decision boundaries in how they categorize the data, which will make them useful in different applications. Implications – The study contributes to an expanding knowledge in how machine learning and wheel motor currents can be used in a collision detection system. The results can lead to lowering production costs and opening new design opportunities. Limitations – The data used in the study is gathered by an autonomous robot which only did frontal collisions on an artificial lawn. Keywords – Machine learning, K-Nearest Neighbour, Multilayer Perceptron, collision detection, autonomous robots, Collison detection based on current. / Syfte – Studiens syfte är att utöka kunskapen om hur hjulmotorstömmar kan kombineras med maskininlärning för att användas vid kollisionsdetektion hos autonoma robotar, detta för att kunna minska antalet krävda externa sensorer hos dessa robotar och på så sätt öppna upp design möjligheter samt minska produktionskostnader Metod – Studien genomfördes med design science research där två artefakter utvecklades i samarbete med Globe Tools Group. Artefakterna utvärderades sedan i hur de kategoriserade kollisioner utifrån en given datamängd som genererades från en autonom gräsklippare. Studiens experiment introducerade sedan in data som inte ingick i samma datamängd för att se hur metoderna kategoriserade detta. Resultat – Artefakterna klarade med 100% noggrannhet att detektera kollisioner i den giva datamängden som genererades. Dock har de två olika artefakterna olika beslutsregioner i hur de kategoriserar datamängderna till kollision samt icke-kollisioner, vilket kan ge dom olika användningsområden Implikationer – Examensarbetet bidrar till en ökad kunskap om hur maskininlärning och hjulmotorströmmar kan användas i ett kollisionsdetekteringssystem. Studiens resultat kan bidra till minskade kostnader i produktion samt nya design möjligheter Begränsningar – Datamängden som användes i studien samlades endast in av en autonom gräsklippare som gjorde frontalkrockar med underlaget konstgräs. Nyckelord – Maskininlärning, K-nearest neighbor, Multi-layer perceptron, kollisionsdetektion, autonoma robotar

For unmanned aircraft systems to gain full access to the National Airspace System (NAS), they must have the capability to detect and avoid other aircraft. This research focuses on the development of a detect-and-avoid (DAA) system for small unmanned aircraft systems. To safely avoid another aircraft, an unmanned aircraft must detect the intruder aircraft with ample time and distance. Two analytical methods for finding the minimum detection range needed are described. The first method, time-based geometric velocity vectors (TGVV), includes the bank-angle dynamics of the ownship while the second, geometric velocity vectors (GVV), assumes an instantaneous bank-angle maneuver. The solution using the first method must be found numerically, while the second has a closed-form analytical solution. These methods are compared to two existing methods. Results show the time-based geometric velocity vectors approach is precise, and the geometric velocity vectors approach is a good approximation under many conditions. The DAA problem requires the use of a robust target detection and tracking algorithm for tracking multiple maneuvering aircraft in the presence of noisy, cluttered, and missed measurements. Additionally these algorithms needs to be able to detect overtaking intruders, which has been resolved by using multiple radar sensors around the aircraft. To achieve these goals the formulation of a nonlinear extension to R-RANSAC has been performed, known as extended recursive-RANSAC (ER-RANSAC). The primary modifications needed for this ER-RANSAC implementation include the use of an EKF, nonlinear inlier functions, and the Gauss-Newton method for model hypothesis and generation. A fully functional DAA system includes target detection and tracking, collision detection, and collision avoidance. In this research we demonstrate the integration of each of the DAA-system subcomponents into fully functional simulation and hardware implementations using a ground-based radar setup. This integration resulted in various modifications of the radar DSP, collision detection, and collision avoidance algorithms, to improve the performance of the fully integrated DAA system. Using these subcomponents we present flight results of a complete ground-based radar DAA system, using actual radar hardware.

Zoomable user interfaces (ZUIs) have been studied for a long time and many applications are built upon them. Most applications, however, only use two dimensions to express the content. This report presents a solution using all three dimensions where the base features are built as a framework with uniform grids and scene graphs as primary data structures. The purpose of these data structures is to improve performance while maintaining flexibility when creating and handling three-dimensional objects. A 3D-ZUI is able to represent the view of the world and its objects in a more lifelike manner. It is possible to interact with the objects much in the same way as in real world. By developing a prototype framework as well as some example applications, the usefulness of 3D-ZUIs is illustrated. Since the framework relies on abstraction and object-oriented principles it is easy to maintain and extend it as needed. The currently implemented data structures are well motivated for a large scale 3D-ZUI in terms of accelerated collision detection and picking and they also provide a flexible base when developing applications. It is possible to further improve performance of the framework, for example by supporting different types of culling and levels of detail

The motion planning problem can be formulated as a Markov decision process (MDP), if the uncertainties in the robot motion and environments can be modeled probabilistically. The complexity of solving these MDPs grow exponentially as the dimension of the problem increases and hence, it is nearly impossible to solve the problem even without constraints. Using hierarchical methods, these MDPs can be transformed into a semi-Markov decision process (SMDP) which only needs to be solved at certain landmark states. In the deterministic robotics motion planning community, sampling based algorithms like probabilistic roadmaps (PRM) and rapidly exploring random trees (RRTs) have been successful in solving very high dimensional deterministic problem. However they are not robust to system with uncertainties in the system dynamics and hence, one of the primary objective of this work is to generalize PRM/RRT to solve motion planning with uncertainty. We first present generalizations of randomized sampling based algorithms PRM and RRT, to incorporate the process uncertainty, and obstacle location uncertainty, termed as "generalized PRM" (GPRM) and "generalized RRT" (GRRT). The controllers used at the lower level of these planners are feedback controllers which ensure convergence of trajectories while mitigating the effects of process uncertainty. The results indicate that the algorithms solve the motion planning problem for a single agent in continuous state/control spaces in the presence of process uncertainty, and constraints such as obstacles and other state/input constraints. Secondly, a novel adaptive sampling technique, termed as "adaptive GPRM" (AGPRM), is proposed for these generalized planners to increase the efficiency and overall success probability of these planners. It was implemented on high-dimensional robot n-link manipulators, with up to 8 links, i.e. in a 16-dimensional state-space. The results demonstrate the ability of the proposed algorithm to handle the motion planning problem for highly non-linear systems in very high-dimensional state space. Finally, a solution methodology, termed the "multi-agent AGPRM" (MAGPRM), is proposed to solve the multi-agent motion planning problem under uncertainty. The technique uses a existing solution technique to the multiple traveling salesman problem (MTSP) in conjunction with GPRM. For real-time implementation, an ?inter-agent collision detection and avoidance? module was designed which ensures that no two agents collide at any time-step. Algorithm was tested on teams of homogeneous and heterogeneous agents in cluttered obstacle space and the algorithm demonstrate the ability to handle such problems in continuous state/control spaces in presence of process uncertainty.

This master's thesis describes the design and implementation of a multi-user web application for scheduling timetables and exams at the Faculty of Information Technology of the Brno University of Technology. This application will provide the user with supporting tools for the manual placement of the timetable windows. Thanks to the algorithm for pre calculating the longest collisions, this application can detect collisions in real time and display information about the number of lectures or exams for students in one day. It will also automatically check the teacher's requirements for the course timetable and automatically allocate exams to rooms. Registered users can create their own timetables and if they think that their version is better than the official one, they can propose it to the administrator directly in the application. Thanks to the history recording mechanism for timetable windows, users will be able to quickly and efficiently return unwanted changes back. In this thesis I will also describe the procedures and problems which occurs in the planning process and all the criteria that the schedule must meet.