Viewfinder: final activity report

The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources. The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation. The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein

Obstacle Avoidance Methods in UAVs

We contributed a method for avoiding obstacles using monocular vision as the only sensor in UAV (Unmaned Aerial vehicle). The vision based ROS (Robotic operating system) node detects the known obstacles in front of the UAV. Unknown obstacles can be taken care of by adding he information of all the obstacles seen in the scene to a map. The distance to obstacle in this research is calculated by just increasing size of the obstacle in front of the UAV. The image processing libraries were used from OpenCV to do thresholding, noise removal and contours detection. This research also tests and evaluate the path planning of UAV using MoveIt architecture, and evaluates the different results obtained.Hence we show the effectiveness of the monocular vision and size as a constraint algorithm in UAVs to detect and avoid frontal obstacles

Self-Driving of a Model Car

Cieľom práce je demonštrácia možností samočinného riadenia modelu vozidla, so zameraním na metódy plánovania lokálnej trajektórie a vyhýbania sa prekážkam. V rámci práce bol model doplnený o výpočtovú platformu Raspberry Pi a vhodné senzory. Konkrétne 2D LiDAR na detekciu a meranie vzdialenosti okolitých objektov, inkrementálny rotačný enkóder na meranie urazenej vzdialenosti a aktuálnej rýchlosti, a gyroskop, ktorý sníma relatívnu orientáciu vozidla. Následne bol implementovaný riadiaci systém schopný prijímať a spracovávať senzorové dáta, využiť ich pri odhade aktuálnej polohy a výpočte optimálnej trajektórie v nezmapovanom prostredí, a podľa parametrov tejto trajektórie ovládať akčné členy na ceste do cieľovej destinácie. Výsledkom je funkčný model vozidla schopný navigácie v neznámom prostredí a dosiahnutia zadaných cieľov jazdou po trajektórii tvorenej dynamicky v závislosti na okolitých prekážkach.The aim of this thesis is to demonstrate options for self-driving model cars, focused on local path planning methods and obstacle avoidance. As a part of the project, the model was supplemented by a computing platform Raspberry Pi and appropriate sensors. Specifically, a 2D LiDAR sensor was used for detection and measuring the distance of surrounding objects, an incremental rotary encoder for measuring the distance travelled and current speed, and a gyroscope to keep track of the vehicle's relative orientation. Subsequently, a control system was implemented. This system is able to receive and process sensor data, use it to estimate vehicle's current location, compute an optimal trajectory in an uncharted environment, and control the vehicle's actuators accordingly. The result is a functional model car able to navigate in an unknown environment and reach specified goals by following a trajectory, dynamically generated depending on the surrounding obstacles.

Fuzzy Free Path Detection from Disparity Maps by Using Least-Squares Fitting to a Plane

A method to detect obstacle-free paths in real-time which works as part of a cognitive navigation aid system for visually impaired people is proposed. It is based on the analysis of disparity maps obtained from a stereo vision system which is carried by the blind user. The presented detection method consists of a fuzzy logic system that assigns a certainty to be part of a free path to each group of pixels, depending on the parameters of a planar-model fitting. We also present experimental results on different real outdoor scenarios showing that our method is the most reliable in the sense that it minimizes the false positives rate.N. Ortigosa acknowledges the support of Universidad Politecnica de Valencia under grant FPI-UPV 2008 and Spanish Ministry of Science and Innovation under grant MTM2010-15200. S. Morillas acknowledges the support of Universidad Politecnica de Valencia under grant PAID-05-12-SP20120696.Ortigosa Araque, N.; Morillas Gómez, S. (2014). 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