A cloud robotics architecture for an emergency management and monitoring service in a smart cityenvironment

Cloud robotics is revolutionizing not only the robotics industry but also the ICT world, giving robots more storage and computing capacity, opening new scenarios that blend the physical to the digital world. In this vision new IT architectures are required to manage robots, retrieve data from them and create services to interact with users. In this paper a possible implementation of a cloud robotics architecture for the interaction between users and UAVs is described. Using the latter as monitoring agents, a service for fighting crime in urban environment is proposed, making one step forward towards the idea of smart cit

Trajectory Tracking of AR.Drone Quadrotor Using Fuzzy Logic Controller

In this paper, Fuzzy Logic Controller (FLC) is implemented in the AR.Drone quadrotor in order to make it follow a given trajectory reference. The distance between the position and angle of the AR.Drone to the reference point is used as the input of FLC. As for the output, pitch value and yaw rate will be the controlling signal for the AR.Drone. The navigation data of the AR.Drone are forward speed (vx), sideward speed (vy), and yaw. These navigation data are going to be used to estimate positions and orientation of the AR.Drone. To compensate the y-position drift, the value of vyis also use as a criterion to determine the roll compensation. The FLC algorithm is implemented to AR.Drone 2.0 Elite Edition using LabVIEW software. Also, the algorithm has been tested in various trajectories such as straight line, a straight line with a perpendicular turn, a rectangular trajectory, and a curved trajectory. The results have shown that AR.Drone can follow a given trajectory with various initial position and orientation quite well

Cooperative Control for Target Tracking with Onboard Sensing

Abstract We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In particular, we do not as-sume that the robot positions are known, but estimate their positions using relative onboard sensing. Our probabilistic localization and control method takes into ac-count the motion and sensing capabilities of the individual robots to minimize the expected future uncertainty of the target position. It reasons about multiple possi-ble sensing topologies and incorporates an efficient topology switching technique to generate locally optimal controls in polynomial time complexity. Simulations show the performance of our approach and prove its flexibility to find suitable sensing topologies depending on the limited sensing capabilities of the robots and the movements of the target. Furthermore, we demonstrate the applicability of our method in various experiments with single and multiple quadrotor robots tracking a ground vehicle in an indoor environment

Backstepping control law application to path tracking with an indoor quadrotor

This paper presents an application of the backstepping control to a path tracking mission using an indoor quadrotor. This study case starts on modeling the quadrotor dynamics in order to design a backstepping control which we applied directly to the Lagrangian dynamic equations. The backstepping control is chosen due to its applicability to this class of nonlinear and under-actuate system. To test the designed control law, a complete quadrotor model identification was performed, using a motion capture system. The procedure used to obtain a good model approximation is presented. Experimental results illustrate the validity of the designed control law, including rich simulations and real indoor flight tests

Master of Science

thesisThis thesis details the development of the Algorithmic Robotics Laboratory, its experimental software environment, and a case study featuring a novel hardware validation of optimal reciprocal collision avoidance. We constructed a robotics laboratory in both software and hardware in which to perform our experiments. This lab features a netted flying volume with motion capture and two custom quadrotors. Also, two experimental software architectures are developed for actuating both ground and aerial robots within a Linux Robot Operating System environment. The first of the frameworks is based upon a single finite state machine program which managed each aspect of the experiment. Concerns about the complexity and reconfigurability of the finite state machine prompted the development of a second framework. This final framework is a multimodal structure featuring programs which focus on these specific functions: State Estimation, Robot Drivers, Experimental Controllers, Inputs, Human Robot Interaction, and a program tailored to the specifics of the algorithm tested in the experiment. These modular frameworks were used to fulfill the mission of the Algorithmic Robotics Lab, in that they were developed to validate robotics algorithms in experiments that were previously only shown in simulation. A case study into collision avoidance was used to mark the foundation of the laboratory through the proving of an optimal reciprocal collision avoidance algorithm for the first time in hardware. In the case study, two human-controlled quadrotors were maliciously flown in colliding trajectories. Optimal reciprocal collision avoidance was demonstrated for the first time on completely independent agents with local sensing. The algorithm was shown to be robust to violations of its inherent assumptions about the dynamics of agents and the ability for those agents to sense imminent collisions. These experiments, in addition to the mathematical foundation of exponential convergence, submits th a t optimal reciprocal collision avoidance is a viable method for holonomic robots in both 2-D and 3-D with noisy sensing. A basis for the idea of reciprocal dance, a motion often seen in human collision avoidance, is also suggested in demonstration to be a product of uncertainty about the state of incoming agents. In the more than one hundred tests conducted in multiple environments, no midair collisions were ever produced

Tag Recognition for Quadcopter Drone Movement

Unmanned Aerial Vehicle (UAV) drone such as Parrot AR.Drone 2.0 is a flying mobile robot which has been popularly researched for the application of search and rescue mission. In this project, Robot Operating System (ROS), a free open source platform for developing robot control software is used to develop a tag recognition program for drone movement. ROS is popular with mobile robotics application development because sensors data transmission for robot control system analysis will be very handy with the use of ROS nodes and packages once the installation and compilation is done correctly. It is expected that the drone can communicate with a laptop via ROS nodes for sensors data transmission which will be further analyzed and processed for the close-loop control system. The developed program consisting of several packages is aimed to demonstrate the recognition of different tags by the drone which will be transformed into a movement command with respect to the tag recognized; in other words, a visual-based navigation program is developed

Simulación del sistema de estabilización de altitud de vuelo en quadcopter

Recently, unmanned aerial vehicles have been an important part of scientific research in various fields. Quadrocopter is an unmanned aerial vehicle with four rotors, two of which rotate clockwise, the other two counterclockwise. Changing the speed of screw rotation allows you to control the movement of the apparatus. The article proposed and tested a mathematical model of a quadcopter. They presented the development of a simple control algorithm that allows to stabilize the height and angular position. The research results show the efficiency of the algorithm and the possibility of its practical implementation. The developed mathematical model can be used instead of a real quadcopter, which will significantly reduce the time during research, as well as avoid the quadrocopter damage, reducing the number of launches.Recientemente, los vehículos aéreos no tripulados han sido una parte importante de la investigación científica en varios campos. Quadrocopter es un vehículo aéreo no tripulado con cuatro rotores, dos de los cuales giran en sentido horario y los otros dos en sentido antihorario. Cambiar la velocidad de rotación del tornillo le permite controlar el movimiento del aparato. El artículo propuso y probó un modelo matemático de un quadcopter. Presentaron el desarrollo de un algoritmo de control simple que permite estabilizar la altura y la posición angular. Los resultados de la investigación muestran la eficiencia del algoritmo y la posibilidad de su implementación práctica. El modelo matemático desarrollado se puede utilizar en lugar de un cuadricóptero real, lo que reducirá significativamente el tiempo durante la investigación, además de evitar el daño del cuadricóptero, reduciendo el número de lanzamientos