Odometry

In this paper, A* path planning algorithm has been represented for a mobile robot to be able to follow a constructed path from its current position to a specified goal within its environment. To ensure that the mobile robot follow the constructed path by path planning algorithm, a motion control algorithm has been built. In the same time, to detect static obstacles and avoid collision with them, an obstacle detection algorithm has been used as a final algorithm that will be used as a part of the whole system to give the robot the ability to move from its initial known position to a specific goal in an optimum way.

En el presente trabajo se presenta el diseño de un sistema de control lineal para el seguimiento de trayectorias de un robot móvil diferencial haciendo uso del sistema operativo robótico ROS en este caso se trabajó con la versión Kinetic Kame. Básicamente el procedimiento se divide en tres etapas: La primera de ellas consiste en la generación de una secuencia de estados deseados, los cuales permitan guiar al robot hacia un punto arbitrario, tomando en consideración restricciones dinámicas y físicas del vehículo. La segunda etapa se encarga de estimar los estados actuales del robot con la ayuda de los sensores a bordo. Por último, en la etapa de control se evalúan los estados actuales del vehículo con respecto a los estados deseados y en base al error se toman decisiones para llevar a cabo acciones correctivas. El controlador propuesto consiste en un control lineal que hace uso del modelo dinámico para calcular las velocidades lineal y angular las cuales se consideran como entradas de control. Las pruebas experimentales se llevan a cabo sobre el robot móvil diferencial Turtlebot3 Waffle PI.

The subject of research is the navigation subsystem of autonomous control system to determine the location and position of agricultural machinery during the movement. The purpose of the work is to develop and research model and algorithms to determine the location and position of mobile agricultural machinery using a physical model. The following tasks are solved in the article: development of agricultural machinery physical model to collect information from sensors during movement, further development and research of applicability of algorithms for location and position determination. The following methods are used: methods of mathematical statistics, methods of information systems theory and data processing, methods of random signals filtration. The following results were obtained: during research, the agricultural machinery physical model to collect information from sensors during movement was created. The model includes a GPS receiver, an accelerometer, gyroscope and infrared encoders, to count the rotation of the wheels, as well as its own four wheelbase of agricultural machinery. The modernized GPS coordinate filtration algorithm using a geochex algorithm is proposed, which according to several successively obtained GPS coordinates calculates the hash received coordinates; if the coordinates have the same hash, it can be argued that the vehicle is in the segment of the area that corresponds to this hash. To determine the physical model position during the movement data from the accelerometer and the gyroscope was processed using Savitzky-Golay and Madgwick filters. With the use of wheels' rotation data, the odometric algorithms for movement and location determining of the agricultural machinery physical model in motion were implemented. Conclusions: to improve the accuracy of estimating the location and position agricultural machinery, algorithms complexation of indicators from different navigation systems should be used to reduce the total error. Research results can be applied in the development of new and modifications of existing navigation subsystems of agricultural machinery autonomous control systems.

One of the most important problems in autonomous robot guidance is their localization, i.e. determining their physical location within their operating area. In this paper we describe hardwre realization of triangulation method for localization of wheeled autonomous robot that operates on flat rectangular surface. Triangulation is a method of calculating location of robot relative to 3 landmarks (beacons) located on fixed predetermined positions. This method usually needs measurement of distances between robot and beacons, to be able to calculate robot position. Different approach based on angle measurement is described in this paper, advantages of this method are discussed and details of hardware realization are explained. System is realized and tested, measurement results are given.

We present a highly effective approach for the calibration of vehicle models. The approach combines the output error technique of system identification theory and the convolution integral solutions of linear systems and stochastic calculus. Rather than calibrate the system differential equation directly for unknown parameters, we calibrate its first integral. This integrated prediction error minimization (IPEM) approach is advantageous because it requires only low-frequency observations of state, and produces unbiased parameter estimates that optimize simulation accuracy for the chosen time horizon. We address the calibration of models that describe both systematic and stochastic dynamics, such that uncertainties can be computed for model predictions. We resolve numerous implementation issues in the application of IPEM, such as the efficient linearization of the dynamics integral with respect to parameters, the treatment of uncertainty in initial conditions, and the formulation of s...

This paper presents a low-cost localization system implementation, suitable for mobile robotics plataform, which combines relative odometrics information with absolute information. Aspects taken into account to acquire the information provided by an optical mouse and a magnetic compass as elements of the position estimate of the robot Giraa_02, are described.

... Dead Reckoning and Cartography Using Stereo Vision for an Autonomous Car ... Preliminary results show that dead reckoning using stationary objects can vastly improve self-localization. ... 0 For visionbased sensors extremely varying illu-mination conditions must be handled. ...

Conventional non-vision based navigation systems relying on purely Global Positioning System (GPS) or inertial sensors can provide the 3D position or orientation of the user. However GPS is often not available in forested regions and cannot be used indoors. Visual odometry provides an independent method to estimate position and orientation of the user/system based on the images captured by the moving user accurately. Vision based systems also provide information (e.g. images, 3D location of landmarks, detection of scene objects) about the scene that the user is looking at. In this project, a set of techniques are used for the accurate pose and position estimation of the moving vehicle for autonomous navigation using the images obtained from two cameras placed at two different locations of the same area on the top of the vehicle. These cases are referred to as stereo vision. Stereo vision provides a method for the 3D reconstruction of the environment which is required for pose and position estimation. Firstly, a set of images are captured. The Harris corner detector is utilized to automatically extract a set of feature points from the images and then feature matching is done using correlation based matching. Triangulation is applied on feature points to find the 3D coordinates. Next, a new set of images is captured. Then repeat the same technique for the new set of images too. Finally, by using the 3D feature points, obtained from the first set of images and the new set of images, the pose and position estimation of moving vehicle is done using QUEST algorithm.

Generally the search and rescue missions in the case of monitoring operations, natural and man-made disasters mainly rely on manned aerial vehicles. But these methods were highly risky for human pilot or rescuers because these included high navigation precision and long operation times, These unmanned aerial vehicles were used only in GPS prone areas based on an inbuilt map. These drawbacks are overcome in this proposed paper by designing a vision-controlled hexacopter which provides vision-controlled flying in GPS-denied environments. It consists of two stages: The onboard stage runs the visual odometry algorithm and the off-board stage runs the localization & mapping algorithm.