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Unmanned Aerial Vehicle Research Facility

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Author: Chowdhary, Girish × Author: Johnson, Eric N. × Type: Text × search.filters.applied.f.resourcesubtype: Article ×

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Now showing 1 - 4 of 4
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    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.
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    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.
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    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.
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    A Process to Obtain Robustness Metrics for Adaptive Flight Controllers
    (Georgia Institute of Technology, 2009-08) Kimbrell, Scott ; Johnson, Eric N. ; Chowdhary, Girish ; Calise, Anthony J. ; Chandramohan, Rajeev
    This research effort seeks a process to draw parallels between the classical stability metrics of gain and phase margins for classical linear control systems with stability margins for adaptive controllers. The method uses a Monte Carlo simulation to yield stability threshold results for the adaptive controller based on problem-specific performance metrics. By fitting a linear controller's analytical robustness results to the adaptive stability data, the gain and phase margin for the performance-fitting linear system are considered to be the worst case equivalent gain and phase margin for the adaptive controller. This paper also discusses some experiences successfully obtaining time delay margin in a flight test setting.