Fluid Dynamics Without Fluids

This chapter will discuss some interesting real applications where Fluid Dynamics equations found fruitful appliances without dealing with "strictly speaking" fluids. In particular, thanks to the large set of analyses performed over different kinds of fluids in different operating and boundary conditions, a wide range of Computational Fluid Dynamics algorithms flourished tackling different aspects, from convergence rate, to stability according to the discretization, to multigrid and linearization problems. This robust and thorough background, both on the- oretical and on practical aspects, made Computational Fluid Dynamics (CFD) appealing also to other sciences and applications where Fluid Dynamics equations, or similar equations very close to them, can be useful in describing complex phenomena not related to fluids. Some applications that will be discussed concern, e.g., Geometry of liquid snowflakes whose con- tour is growing steered by curvature, staring from a circle. Furthermore Image Restoration and Segmentation can also benefit from CFD since a set of evolutionary algorithms, based on level-set curvature flow equations, plays a fundamental role in steering active contours or snakes through the noise present in the image till the complete warping of the desired framed object. Also in this case advanced techniques like Ghost Fluids Method for two competing fluids dynamics can be used to separate different objects in images. Other interesting appli- cations that will be described concern applicability of CFD to surface extraction from cloud of points. This is a common problem when complex clouds of points, representing 3D objects or scenes are obtained by laser scanners or multi-camera vision systems. These points represent unambiguous features from corners or sharp edges and the final 3D closed surface must fit on these points smoothly interpolating empty space between them. Also in this case CFD can provide useful tools to define the evolution of a 3D surface representing the border between two competing fluids, one representing the "inside" and the other the "outside" of the object itself. The two fluids evolution will stop when surface sticks on all the 3D points: the viscosity of the two fluids will control the smoothness of this surface that will wrap the cloud and tur- bulence is used to model injection into grooves or narrow holes. This chapter will also discuss another interesting application of CFD to robotic navigation in complex environments where we are looking for the best path, both in terms of length and distance from objects, through a set of obstacles, different terrains traversability or path slope. Also in this case an imaginary fluid with a predefined viscosity floods from the robot position through the whole environ- ment, its front evolution speed, accordingly to CFD, will be slower in narrow passages and, once it reaches the target, it will define the easiest way

Unaprijeđeni algoritam za praćenje putanje na neravnoj cesti

The path planning problem is an important problem in the research area of robot, games and group animation. This paper shows a 2.5-dimensional terrain grid which can reduce the amount of computation. By applying the fuzzy logic theory, the terrain trafficability of the rugged road can be evaluated based on different gradient, roughness, elevation difference; the trafficability factor can be achieved and applied to the heuristic function. The improved algorithm can solve the symmetry problem of path planning on uneven surfaces, reduce the search space.Problem planirana putanje je važan problem u istraživačkom području robotike, igara i grupne animacije. U ovom radu teren je predstavljen 2.5-dimenzionalnom mrežom što može smanjiti vrijeme računanja. Korištenjem teorije neizrazite logike prohodnost neravne ceste može se procijeniti na osnovu razlike gradijenata, nagiba i grbavosti, te se može odrediti faktor prohodnosti koji je primijenjiv na heurističku funkciju. Unaprijeđeni algoritam može riješiti problem simetrije kod planiranja putanje na neravnim površinama i smanjiti prostor pretraživanja

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU

Fuzzy behaviors for mobile robot navigation: design, coordination and fusion

AbstractThe implementation of complex behavior generation for artificial systems can be overcome by decomposing the global tasks into simpler, well-specified behaviors which are easier to design and can be tuned independently of each other. Robot behavior can be implemented as a set of fuzzy rules which mimic expert knowledge in specific tasks in order to model expert knowledge. These behaviors are included in the lowest level of a hybrid deliberative–reactive architecture which is aimed at an efficient integration of planning and reactive control. In this work, we briefly present the architecture and attention is focused on the design, coordination and fusion of the elementary behaviors. The design is based on regulatory control using fuzzy logic control and the coordination is defined by fuzzy metarules which define the context of applicability for each behavior. Regarding action fusion, two combination methods for fusing the preferences from each behavior are used in the experiments. In order to validate the system, several measures are also proposed, and thus the performance of the architecture and combination/arbitration algorithms have been demonstrated in both the simulated and the real world. The robot achieves every control objective and the trajectory is smooth in spite of the interaction between several behaviors, unexpected obstacles and the presence of noisy data. When the results of the experimentation from both methods are taken into account, the influence of the combination method appears to be of prime importance when attempting to achieve the best trade-off among the preferences of every behavior

Collaborator: A Nonholonomic Multiagent Team for Tasks in a Dynamic Environment

In our previous work, we proposed a potential field-based hybrid path planning scheme for robot navigation that achieves complete coverage in various tasks. This paper is an extension of this work producing a multiagent framework, Collaborator, that integrates a high-level negotiation-based task allocation protocol with a low-level path planning method taking into consideration several real-world robot limitations such as nonholonomic constraints. Specifically, the proposed framework focuses on a class of complex motion planning problems in which robots need to cover the whole workspace, coordinate the accomplishment of a task, and dynamically change their roles to best fit the task. Applications in this class of problems include bomb detection and removal as well as rescuing of survivors from accidents or disasters. We have tested the framework in simulations of several tasks and have shown that Collaborator can satisfy nonholonomic constraints, cooperatively accomplish given tasks in an initially unknown dynamic environment while avoiding collision with other team members. Finally we prove that the proposed control laws are stable using the Lyapunov stability theory

Design and analysis of Intelligent Navigational controller for Mobile Robot

Since last several years requirement graph for autonomous mobile robots according to its virtual application has always been an upward one. Smother and faster mobile robots navigation with multiple function are the necessity of the day. This research is based on navigation system as well as kinematics model analysis for autonomous mobile robot in known environments. To execute and attain introductory robotic behaviour inside environments(e.g. obstacle avoidance, wall or edge following and target seeking) robot uses method of perception, sensor integration and fusion. With the help of these sensors robot creates its collision free path and analyse an environmental map time to time. Mobile robot navigation in an unfamiliar environment can be successfully studied here using online sensor fusion and integration. Various AI algorithm are used to describe overall procedure of mobilerobot navigation and its path planning problem. To design suitable controller that create collision free path are achieved by the combined study of kinematics analysis of motion as well as an artificial intelligent technique. In fuzzy logic approach, a set of linguistic fuzzy rules are generated for navigation of mobile robot. An expert controller has been developed for the navigation in various condition of environment using these fuzzy rules. Further, type-2 fuzzy is employed to simplify and clarify the developed control algorithm more accurately due to fuzzy logic limitations. In addition, recurrent neural network (RNN) methodology has been analysed for robot navigation. Which helps the model at the time of learning stage. The robustness of controller has been checked on Webots simulation platform. Simulation results and performance of the controller using Webots platform show that, the mobile robot is capable for avoiding obstacles and reaching the termination point in efficient manner

Visually guided obstacle detection and avoidance for legged robot.

Chow Ying-ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 78-83).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives - Visual Navigation for Legged Robots --- p.1Chapter 1.2 --- Summary of Results --- p.3Chapter 1.3 --- Hardware Issues --- p.4Chapter 1.4 --- Contributions --- p.4Chapter 1.5 --- Organization of the Thesis --- p.4Chapter Chapter 2 --- Previous Work --- p.6Chapter 2.1 --- Vision Based Navigation --- p.6Chapter 2.1.1 --- Homography --- p.7Chapter 2.1.2 --- Ground Plane Obstacle Detection --- p.9Chapter 2.1.3 --- Regression --- p.12Chapter 2.2 --- Control Strategy --- p.13Chapter Chapter 3 --- System Overview --- p.16Chapter Chapter 4 --- Obstacle Detection by Fast Homography Estimation --- p.20Chapter 4.1 --- Ground Feature Extraction --- p.21Chapter 4.2 --- Ground Feature Correspondence --- p.21Chapter 4.3 --- Ground Homography Estimation --- p.24Chapter 4.3.1 --- Input point transformation --- p.24Chapter 4.3.2 --- Initial estimation --- p.26Chapter 4.3.3 --- Robust estimation --- p.27Chapter 4.4 --- Obstacle Detection --- p.29Chapter 4.5 --- Local Obstacle Map (LOM) on Ground --- p.33Chapter 4.5.1 --- Extraction from accumulative evidence --- p.34Chapter 4.5.2 --- Time-delay compensation --- p.34Chapter Chapter 5 --- Obstacle Avoidance by a Fuzzy Controller --- p.36Chapter 5.1 --- Gait Pattern --- p.38Chapter 5.2 --- Fuzzy Logic Controller --- p.42Chapter 5.2.1 --- Controller Inputs --- p.42Chapter 5.2.2 --- Controller Outputs --- p.43Chapter 5.2.3 --- Inference mechanism --- p.46Chapter Chapter 6 --- Implementation --- p.49Chapter 6.1 --- Hardware components --- p.49Chapter 6.1.1 --- VisionBug --- p.49Chapter 6.1.2 --- RF transmitter / receiver modules: --- p.52Chapter 6.2 --- Perception --- p.55Chapter 6.3 --- Image Calibration --- p.56Chapter 6.4 --- Motion Calibration: --- p.58Chapter 6.5 --- Software Programs --- p.66Chapter 6.5.1 --- Computational complexity --- p.68Chapter Chapter 7 --- Experimental Results --- p.69Chapter 7.1 --- Real Navigation Experiments --- p.70Chapter 7.2 --- Error Analysis of LOM --- p.73Chapter Chapter 8 --- Conclusion and future work --- p.7