5,001 research outputs found
A practical multirobot localization system
We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems
Advances in Human Robot Interaction for Cloud Robotics applications
In this thesis are analyzed different and innovative techniques for Human Robot Interaction. The focus of this thesis is on the interaction with flying robots. The first part is a preliminary description of the state of the art interactions techniques. Then the first project is Fly4SmartCity, where it is analyzed the interaction between humans (the citizen and the operator) and drones mediated by a cloud robotics platform. Then there is an application of the sliding autonomy paradigm and the analysis of different degrees of autonomy supported by a cloud robotics platform. The last part is dedicated to the most innovative technique for human-drone interaction in the User’s Flying Organizer project (UFO project). This project wants to develop a flying robot able to project information into the environment exploiting concepts of Spatial Augmented Realit
Toward safe and stable time-delayed mobile robot teleoperation through sampling-based path planning
This work proposes a teleoperation architecture for mobile robots in partially unknown environments under the presence of variable time delay. The system is provided with artificial intelligence represented by a probabilistic path planner that, in combination with a prediction module, assists the operator while guaranteeing a collision-free motion. For this purpose, a certain level of autonomy is given to the system. The structure was tested in indoor environments for different kinds of operators. A maximum time delay of 2s was successfully coped with. © 2011 Cambridge University Press
UWB system and algorithms for indoor positioning
This research work presents of study of ultra-wide band (UWB) indoor positioning
considering different type of obstacles that can affect the localization accuracy. In the
actual warehouse, a variety of obstacles including metal, board, worker and other
obstacles will have NLOS (non-line-of-sight) impact on the positioning of the logistics
package, which influence the measurement of the distance between the logistics package
and the anchor , thereby affecting positioning accuracy. A new developed method
attempts to improve the accuracy of UWB indoor positioning, through and improved
positioning algorithm and filtering algorithm. In this project, simulate the warehouse
environment in the laboratory, several simulation proves that the used Kalman filter
algorithm and Markov algorithm can effectively reduce the error of NLOS. Experimental
validation is carried out considering a mobile tag mounted on a robot platform.Este trabalho de pesquisa apresenta um estudo de posicionamento de banda ultra-larga
(UWB) em ambientes internos considerando diferentes tipos de obstáculos que podem
afetar a precisão de localização. No armazém real, uma variedade de obstáculos incluindo
metal, placa, trabalhador e outros obstáculos terão impacto NLOS (não linha de visão) no
posicionamento do pacote logístico, o que influencia a medição da distância entre o
pacote logístico e a âncora, afetando assim a precisão do posicionamento. Um novo
método desenvolvido tenta melhorar a precisão do posicionamento interno UWB, através
de um algoritmo de posicionamento e algoritmo de filtragem aprimorados. Neste projeto,
para simular o ambiente de warehouse em laboratório, diversas simulações comprovam
que o algoritmo de filtro de Kalman e o algoritmo de Markov usados podem efetivamente
reduzir o erro de NLOS. A validação experimental é realizada considerando um tag móvel
montado em uma plataforma de robô
Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings
Air quality in many poultry buildings is less than desirable. However, the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult. To counter this, the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated. This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors. The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor. The inertial measurement unit (IMU) was rigidly fixed on the robot vehicle platform. The research focused on using the internal sensors to calculate the robot position (x, y, θ) through three different methods. The first method relied only on odometer dead reckoning (ODR), the second method was the combination of odometer and gyroscope data dead reckoning (OGDR) and the last method was based on Kalman filter data fusion algorithm (KFDF). A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy. These tests were conducted on different types of surfaces and path profiles. The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate. However, improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate. The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations. The ground type was also found to be an influencing factor of localisation errors
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
Design and implementation control system for a self-balancing robot based on internet of things by using Arduino microcontroller
This project is designed for attempting on developing an autonomous self-balancing robot. In this work, the two-wheel robotic system consists of a microcontroller (Arduino), Dc motor, and sensor. The Arduino is used to read the sensor data and gives the order of the motor based on the control algorithm to remaine the system is stable at different impediment. The robot is drive with Dc motor and the Arduino cannot drive. A motor driver (L298 type) is used to provide a sufficient current. The Ultrasonic sensor (used to sense impediment during the movement) and 3-axis gyroscope accelerometer sensor (To measure the robot inclination angle) to control the two-wheel robot. The controller laws allow reaching static or moving targets based on three structured IOT interactions between the elementary controllers and the sensor with actuator via Cloud environment. Regarding the technical detail must be designed based on the mathematical model. The mathematical model is used based on the model of some references, after that, the transfer function of the system is found. In this work, the MATLAB Simulink is used in the design of the controller, and the PID controller is used due to the simplicity and good activity in central systems. The PID tuner package Simulink is used to obtain the controller parameter (kp, ki, kd) that gives fast and good system response and stability. The result of the designed controller shows that the system has remained stable (remained vertically) and very fast (less than 1sec) until the system reaches the desired output
- …