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Unmanned Aerial Vehicle Research Facility
Unmanned Aerial Vehicle Research Facility
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ItemGPS-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, AndyGPS-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.
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ItemAutonomous 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, GirishA 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.
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ItemSelf-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.
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ItemIntegrated 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.
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ItemGeorgia 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.
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ItemNetwork 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.
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ItemDesign, 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.