In this paper we present a navigation system for Micro Aerial Vehicles (MAV) based on information provided by a visual odometry algorithm processing data from an RGB-D camera. The visual odometry algorithm uses an uncertainty analysis of the depth information to align newly observed features against a global sparse model of previously detected 3D features. The visual odometry provides updates at roughly 30 Hz that is fused at 1 KHz with the inertial sensor data through a Kalman Filter. The high-rate pose estimation is used as feedback for the controller, enabling autonomous flight. We developed a 4DOF path planner and implemented a real-time 3D SLAM where all the system runs on-board. The experimental results and live video demonstrates the autonomous flight and 3D SLAM capabilities of the quadrotor with our system.

Autonomous quadrotor flight using onboard RGB-D visual odometry

In this paper we explore the combination of a latest generation mobile device and a micro quadrotor platform to perform indoor autonomous navigation for the purpose of autonomous photography. We use the Yellowstone tablet from Google's Tango project [1], equipped with onboard, fully integrated sensing platform and with significant computational capability. To the best of our knowledge we are the first to exploit the Google's Tango tablet as source of pose estimate to control the quadrotor's motion. Using the tablet's onboard camera the system is able to detect people and generate a desired pose that the quadrotor will have to reach in order to take a well framed picture of the detected subject. The experimental results and live video demonstrate the capabilities of the autonomous flying robot photographer using the system described throughout this manuscript.

An autonomous flyer photographer

Detection and prevention of faults in overhead electric lines is critical for the reliability and availability of electricity supply. The disadvantages of conventional methods range from cumbersome installations to costly maintenance and from lack of adaptability to hazards for human operators. Thus, transmission inspections based on unmanned aerial vehicles (UAV) have been attracting the attention of researchers since their inception. This article provides a comprehensive review for the development of UAV technologies in the overhead electric power lines patrol process for monitoring and identifying faults, explores its advantages, and realizes the potential of the aforementioned method and how it can be exploited to avoid obstacles, especially when compared with the state-of-the-art mechanical methods. The review focuses on the development of advanced Learning Control strategies for higher manoeuvrability of the quadrotor. It also explores suitable recharging strategies and motor control for improved mission autonomy.

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An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

A single tilt-wing UAV has a new-concept hybrid configuration for adopting the advantages of both fixed-wing and rotary-wing aircraft. This paper presents a dynamic modeling and analysis of the single tilt-wing UAV which is composed of a tilted main wing, a fuselage, a tail wing, and a variable pitch propeller. The main wing can be divided into tilted and fixed section. The tilted section is located at outer wing and has an electric propulsive system at each side. The variable pitch system is used for improving control performance during vertical flight. The dynamic modeling is derived by forces and moments analysis of the UAV. Momentum theory and blade element theory are used for thrust term, and a wetted and an unwetted area are considered for the precise main wing model. The force and moment variation is considered for tilting angle change from 0 to 90 degrees: cruise to hover mode. Also, trim conditions are analyzed for each flight mode, and flight dynamic characteristics are presented through open-loop pole location with respect to trim conditions.

Dynamic Modeling and Analysis of a Single Tilt-Wing Unmanned Aerial Vehicle

Vision systems in drones, specifically Unmanned Aerial Vehicles (UAV) are currently used for retrieving real-time video of the area to fly over, nevertheless commercially there are no identification nor control applications for this type of aircrafts. Some existing technologies for trajectory generation are based on cubic polynomials and Bezier curves [5] but this paper presents the implementation of an optical flow algorithm that assures to track and control the trajectory in a quadrotor drone in real time, determining the distance between the drone and a certain objective as well as the speed calculation and its displacement direction by the means of the implementation of the Lucas-Kanade (LK) algorithm. The core purpose of this paper is to achieve a low-cost and faster (due to the UAV's dynamics) solution besides the implementation of a Global Positioning System (GPS) using an Inertial Navigation System (INS) to generate an aerial trajectory, therefore it can be generated in real time converting the UAV into a semi-autonomous aircraft. The aerial robot's dynamic model was obtained by the Euler-Lagrange method, the image processing is based on the pin-hole model of a camera and optical flow. The main advantages and results obtained through the experimentation was first to correctly detect the line, and by LK implementation on OpenCV the line was correctly tracked by the drone achieving this with a low computational cost and with the possibility of indoor use covering the applications where the GPS is not able to work.

Real-Time Drone (UAV) Trajectory Generation and Tracking by Optical Flow

Stereo vision is a broad field that is part of computer vision. Autonomous mobile robots make use of stereo vision to detect obstacles and measure their relative distance to them for path planning. Technically, it consists of the processing of two images simultaneously adquired by two cameras, from a given scene. Stereo vision is a field with multiple applications and many resources had been invested for research in this subject. In this paper, we present a stereo vision based controller design and its implementation on a mini helicopter with 4 rotors known as quadrotor. The dynamic model is obtained using the well known Euler-Lagrange approach. Experiment results show good performance of the proposed controller using "real-time" optical flow and image processing from two cameras.

Stereo vision for the stabilization of a quadrotor

This paper presents the mathematical model of a fixed wing UAV (Unmanned Aerial Vehicle). The UAV is not a tail-sitter configuration and takes-off with the fuselage at the horizontal position. An experimental prototype driven by four brushless motors has been built including the onboard avionics. The applied control law is based on separated saturation functions. Numerical simulations have shown the satisfactory performance of the proposed control law. The prototype has also been tested experimentally in hover only and the control strategy has achieved that orientation angles have been regulated close to the origin.

Modeling and control of a vertical take-off airplane

This paper presents the mathematical model of a fixed wing mini-UAV (Unmanned Aerial Vehicle). The mini-UAV is not a tail-sitter configuration and takes-off with the fuselage at the horizontal position. An experimental prototype driven by four brushless motors has been built including the onboard avionics. The applied control law is based on separated saturation functions algorithm. Numerical simulations have shown the satisfactory performance of the proposed control law in vertical take-off and hover. The prototype has also been tested experimentally in hover only and the control strategy has achieved that orientation angles have been regulated close to the origin.

V. Rosas

Papers

Stereo vision for the stabilization of a quadrotor

Modeling and control of a vertical take-off airplane

Modeling and control of a convertible airplane