Organizational Unit:

Unmanned Aerial Vehicle Research Facility

Permanent Link

https://hdl.handle.net/1853/70748

Parent Organization

Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

ArchiveSpace Name Record

https://finding-aids.library.gatech.edu/agents/corporate_entities/1139

Full item page

Publication Search Results

Now showing 1 - 10 of 13

GPS-denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

(Georgia Institute of Technology, 2013-05) Chowdhary, Girish ; Johnson, Eric N. ; Magree, Daniel ; Wu, Allen ; Shein, Andy

GPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INSs) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INSs can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPS-denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource-constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16-min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, and gusty indoor environment.
Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors

(Georgia Institute of Technology, 2013-04) Wu, Allen D. ; Johnson, Eric N. ; Kaess, Michael ; Dellaert, Frank ; Chowdhary, Girish

A vision-aided inertial navigation system that enables autonomous flight of an aerial vehicle in GPS-denied environments is presented. Particularly, feature point information from a monocular vision sensor are used to bound the drift resulting from integrating accelerations and angular rate measurements from an Inertial Measurement Unit (IMU) forward in time. An Extended Kalman filter framework is proposed for performing the tasks of vision-based mapping and navigation separately. When GPS is available, multiple observations of a single landmark point from the vision sensor are used to estimate the point’s location in inertial space. When GPS is not available, points that have been sufficiently mapped out can be used for estimating vehicle position and attitude. Simulation and flight test results of a vehicle operating autonomously in a simplified loss-of-GPS scenario verify the presented method.
Self-Contained Autonomous Indoor Flight with Ranging Sensor Navigation

(Georgia Institute of Technology, 2012-11) Chowdhary, Girish ; Sobers, D. Michael, Jr. ; Pravitra, Chintasid ; Christmann, Hans Claus ; Wu, Allen ; Hashimoto, Hiroyuki ; Ong, Chester ; Kalghatgi, Roshan ; Johnson, Eric N.

This paper describes the design and flight test of a completely self-contained autonomous indoor Miniature Unmanned Aerial System (M-UAS). Guidance, navigation, and control algorithms are presented, enabling the M-UAS to autonomously explore cluttered indoor areas without relying on any off-board computation or external navigation aids such as GPS. The system uses a scanning laser rangefinder and a streamlined Simultaneous Localization and Mapping (SLAM) algorithm to provide a position and heading estimate, which is combined with other sensor data to form a six degree-of-freedom inertial navigation solution. This enables an accurate estimate of the vehicle attitude, relative position, and velocity. The state information, with a self-generated map, is used to implement a frontier-based exhaustive search of an indoor environment. Improvements to existing guidance algorithms balance exploration with the need to remain within sensor range of indoor structures such that the SLAM algorithm has sufficient information to form a reliable position estimate. A dilution of precision metric is developed to quantify the effect of environment geometry on the SLAM pose covariance, which is then used to update the 2-D position and heading in the navigation filter. Simulation and flight test results validate the presented algorithms.
Integrated Guidance Navigation and Control for a Fully Autonomous Indoor UAS

(Georgia Institute of Technology, 2011-08) Chowdhary, Girish ; Sobers, D. Michael, Jr. ; Pravitra, Chintasid ; Christmann, Hans Claus ; Wu, Allen ; Hashimoto, Hiroyuki ; Ong, Chester ; Kalghatgi, Roshan ; Johnson, Eric N.

This paper describes the details of a Quadrotor miniature unmanned aerial system capable of autonomously exploring cluttered indoor areas without relying on any external navigational aids such as GPS. A streamlined Simultaneous Localization and Mapping (SLAM) algorithm is implemented onboard the vehicle to fuse information from a scanning laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. This state information, with a self-generated map, is used to implement a frontier-based exhaustive search of an indoor environment. To ensure the SLAM algorithm has sufficient information to form a reliable solution, the guidance algorithm ensures the vehicle approaches frontier waypoints through a path that remains within sensor range of indoor structures. Along with a detailed description of the system, simulation and hardware testing results are presented.
Georgia Tech Team Entry for the 2011 AUVSI International Aerial Robotics Competition

(Georgia Institute of Technology, 2011-08) Chowdhary, Girish ; Magree, Daniel ; Bershadsky, Dmitry ; Dyer, Timothy ; George, Eohan ; Hashimoto, Hiroyuki ; Kalghatgi, Roshan ; Johnson, Eric N.

his paper describes the details of a Quadrotor Unmanned Aerial Vehicle capable of exploring cluttered indoor areas without relying on any external navigational aids. An elaborate Simultaneous Localization and Mapping (SLAM) algorithm is used to fuse information from a laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. A wall-following guidance rule is implemented to ensure that the vehicle explores maximum indoor area in a reasonable amount of time. A model reference adaptive control architecture is used to ensure stability and mitigation of uncertainties. The vehicle is intended to be Georgia Tech Aerial Robotic Team's entry for the 2011 International Aerial Robotics Competition (IARC) Symposium on Indoor Flight Issues.
Network Discovery: An Estimation Based Approach

(Georgia Institute of Technology, 2011-06) Chowdhary, Girish ; Egerstedt, Magnus B. ; Johnson, Eric N.

We consider the unaddressed problem of network discovery, in which, an agent attempts to formulate an estimate of the global network topology using only locally available information. We show that under two key assumptions, the network discovery problem can be cast as a parameter estimation problem. Furthermore, we show that some form of excitation must be present in the network to be able to converge to a solution. The performance of two methods for solving the network discovery problem is evaluated in simulation.
Design, Development, and Testing of a Low Cost, Fully Autonomous Indoor Unmanned Aerial System

(Georgia Institute of Technology, 2010-08) Chowdhary, Girish ; Sobers, D. Michael Jr. ; Salaün, Erwan ; Ottander, John ; Johnson, Eric N.

This paper is concerned with the design, development, and autonomous flight testing of the GT Lama indoor Unmanned Aerial System (UAS). The GT Lama is a fully autonomous rotorcraft UAS capable of indoor area exploration. It weighs around 1.3 lbs (600 gms), has a width of about 27.6 inches (70 cm), and costs less than USD 900. The GT Lama employs only five off-the-shelf, extremely low-cost range sensors for navigation. The GT Lama does not rely on other expensive and sophisticated sensors, including inertial measurement units, Laser based range scanners, and GPS. The GT Lama achieves this by using simple wall following logic to ensure that maximum perimeter of an indoor environment is explored in a reasonable amount of time. The GT Lama hardware, and the Guidance, Navigation, and Control (GNC) algorithms used are discussed in detail. The details of a MATLAB based method that facilitates rapid in flight validation of GNC algorithms on real flight hardware is also discussed. Results from flight tests as the GT Lama autonomously explores indoor environments are presented.
Flight Test Validation of a Neural Network based Long Term Learning Adaptive Flight Controller

(Georgia Institute of Technology, 2009-08) Chowdhary, Girish ; Johnson, Eric N.

The purpose of this paper is to present and analyze flight test results of a Long Term Learning Adaptive Flight Controller implemented on a rotorcraft and a fixed wing Unmanned Aerial Vehicle. The adaptive control architecture used is based on a proven Model Reference Adaptive Control (MRAC) architecture employing a Neural Network as the adaptive element. The method employed for training the Neural Network for these flight tests is unique since it uses current (online) as well as stored (background) information concurrently for adaptation. This ability allows the adaptive element to simulate long term memory by retaining specifically stored input output data pairs and using them for concurrent adaptation. Furthermore, the structure of the adaptive law ensures that concurrent training on past data does not affect the responsiveness of the adaptive element to current data. The results show that the concurrent use of current and background data does not affect the practical stability properties of the MRAC control architecture. The results also confirm expected improvements in performance.
Indoor Navigation for Unmanned Aerial Vehicles

(Georgia Institute of Technology, 2009-08) Sobers, D. Michael Jr. ; Chowdhary, Girish ; Johnson, Eric N.

The ability for vehicles to navigate unknown environments is critical for autonomous operation. Mapping of a vehicle's environment and self-localization within that environment are especially difficult for an Unmanned Aerial Vehicle (UAV) due to the complexity of UAV attitude and motion dynamics, as well as interference from external influences such as wind. By using a stable vehicle platform and taking advantage of the geometric structure typical of most indoor environments, the complexity of the localization and mapping problem can be reduced. Interior wall and obstacle location can be measured using low-cost range sensors. Relative vehicle location within the mapped environment can then be determined. By alternating between mapping and localization, a vehicle can explore its environment autonomously. This paper examines available low-cost range sensors for suitability in solving the mapping and localization problem. A control system and navigation algorithm are developed to perform mapping of indoor environments and localization. Simulation and experimental results are provided to determine feasibility of the proposed approach to indoor navigation.
Real-Time System Identification of a Small Multi-Engine Aircraft

(Georgia Institute of Technology, 2009-08) DeBusk, Wesley M. ; Chowdhary, Girish ; Johnson, Eric N.

In-flight identification of an aircraft's dynamic model can benefit adaptive control schemes by providing estimates of aerodynamic stability derivatives in real time. This information is useful when the dynamic model changes severely in flight such as when faults and failures occur. Moreover a continuously updating model of the aircraft dynamics can be used to monitor the performance of onboard controllers. Flight test data was collected using a sum of sines input implemented in closed loop on a twin engine, fixed wing, Unmanned Aerial Vehicle. This data has been used to estimate a complete six degree of freedom aircraft linear model using the recursive Fourier Transform Regression method in frequency domain. The methods presented in this paper have been successfully validated using computer simulation and real flight data. This paper shows the feasibility of using the frequency domain Fourier Transform Regression method for real time parameter identification.