Organizational Unit:

Unmanned Aerial Vehicle Research Facility

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

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Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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

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Publication Search Results

Now showing 1 - 10 of 20

Adaptive Position and Attitude-Tracking Controller for Satellite Proximity Operations Using Dual Quaternions

(Georgia Institute of Technology, 2015) Filipe, Nuno ; Tsiotras, Panagiotis

This paper proposes a nonlinear adaptive position and attitude tracking controller for satellite proximity operations between a target and a chaser satellite. The controller requires no information about the mass and inertia matrix of the chaser satellite, and takes into account the gravitational acceleration, the gravity-gradient torque, the perturbing acceleration due to Earth's oblateness, and constant - but otherwise unknown - disturbance forces and torques. Sufficient conditions to identify the mass and inertia matrix of the chaser satellite are also given. The controller is shown to ensure almost global asymptotical stability of the translational and rotational position and velocity tracking errors. Unit dual quaternions are used to simultaneously represent the absolute and relative attitude and position of the target and chaser satellites. The analogies between quaternions and dual quaternions are explored in the development of the controller.
Collaborative Mapping and Search for Autonomous Helicopters

(Georgia Institute of Technology, 2013-05) Johnson, Eric N. ; Magree, Daniel P. ; Mooney, John G. ; White, Matthew ; Hartman, Jonathan ; Sahasrabudhe, Vineet

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a multi-aircraft collaborative architecture, focused on decentralized autonomous decision-making. The architecture includes a finite-state machine, Voronoi mapping strategy, and real-time information sharing system designed to solve a challenge problem. The architecture was implemented on a pair of Yamaha RMax helicopters outfitted with modular avionics, as well as an associated set of simulation tools. Simulation results for single- and multiple-aircraft scenarios are presented, along with a quadratic relationship between mapping speed and task completion time. Further work suggested includes validation of simulation results in flight test with two real aircraft, as well as further exploration between search problem parameters and theoretical optimal performance.
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.
Optimal Feedback Guidance of a Small Aerial Vehicle in the Presence of Stochastic Wind

(Georgia Institute of Technology, 2013) Anderson, Ross P. ; Bakolas, Efstathios ; Milutinović, Dejan ; Tsiotras, Panagiotis

The navigation of a small unmanned aerial vehicle is challenging due to a large influence of wind to its kinematics. When the kinematic model is reduced to two dimensions, it has the form of the Dubins kinematic vehicle model. Consequently, this paper addresses the problem of minimizing the expected time required to drive a Dubins vehicle to a prescribed target set in the presence of a stochastically varying wind. First, two analytically-derived control laws are presented. One control law does not consider the presence of the wind, whereas the other assumes that the wind is constant and known a priori. In the latter case it is assumed that the prevailing wind is equal to its mean value; no information about the variations of the wind speed and direction is available. Next, by employing numerical techniques from stochastic optimal control, feedback control strategies are computed. These anticipate the stochastic variation of the wind and drive the vehicle to its target set while minimizing the expected time of arrival. The analysis and numerical simulations show that the analytically-derived deterministic optimal control for this problem captures, in many cases, the salient features of the optimal feedback control for the stochastic wind model, providing support for the use of the former in the presence of light winds. On the other hand, the controllers anticipating the stochastic wind variation lead to more robust and more predictable trajectories than the ones obtained using feedback controllers for deterministic wind models.
Terrain Height Evidence Sharing for Collaborative Autonomous Rotorcraft Operation

(Georgia Institute of Technology, 2013-01) Johnson, Eric N. ; Mooney, John G. ; White, Matthew ; Hartman, Jonathan ; Sahasrabudhe, Vineet

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time information sharing system to support collaborative autonomy and high performance nap-of-the-Earth helicopter flight. The emphasis here is on smart and selective sharing of terrain data which (1) minimizes the bandwidth consumed by obstacle/terrain-information-sharing between aircraft, (2) assigns an appropriate level of confidence to the data received from other heterogeneous aircraft, (3) is robust to sensor error and failures, and (4) is robust to entry and exit of vehicles from the network. Results from simulation and flight testing are provided.
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.
Relay Pursuit of a Maneuvering Target Using Dynamic Voronoi Diagrams

(Georgia Institute of Technology, 2012-08) Bakolas, Efstathios ; Tsiotras, Panagiotis

This paper addresses the problem of the pursuit of a maneuvering target by a group of pursuers distributed in the plane. This pursuit problem is solved by associating it with a Voronoi-like partitioning problem that characterizes the set of initial positions from which the target can be intercepted by a given pursuer faster than any other pursuer from the same group. In the formulation of this partitioning problem, the target does not necessarily travel along prescribed trajectories, as it is typically assumed in the literature, but, instead, it can apply an “evading” strategy in an effort to delay or, if possible, escape capture. We characterize an approximate solution to this problem by associating it with a standard Voronoi partitioning problem. Subsequently, we propose a relay pursuit strategy, that is, a special group pursuit scheme such that, at each instant of time, only one pursuer is assigned the task of capturing the maneuvering target. During the course of the relay pursuit, the pursuer-target assignment changes dynamically with time based on the (time varying) proximity relations between the pursuers and the target. This proximity information is encoded in the solution of the Voronoi-like partitioning problem. Simulation results are presented to highlight the theoretical developments.
Optimal Synthesis of the Zermelo–Markov–Dubins Problem in a Constant Drift Field

(Georgia Institute of Technology, 2012-01-16) Bakolas, Efstathios ; Tsiotras, Panagiotis

We consider the optimal synthesis of the Zermelo–Markov–Dubins problem, that is, the problem of steering a vehicle with the kinematics of the Isaacs–Dubins car in minimum time in the presence of a drift field. By using standard optimal control tools, we characterize the family of control sequences that are sufficient for complete controllability and necessary for optimality for the special case of a constant field. Furthermore, we present a semi-analytic scheme for the characterization of a (nearly) optimal synthesis of the minimum-time problem. Finally, we establish a direct correspondence between the optimal syntheses of the Markov–Dubins and the Zermelo–Markov–Dubins problems by means of a discontinuous mapping.
Feedback Navigation in an Uncertain Flow Field and Connections with Pursuit Strategies

(Georgia Institute of Technology, 2012) Bakolas, Efstathios ; Tsiotras, Panagiotis

This paper presents several classes of control laws for steering an agent, that is, an aerial or marine vehicle, in the presence of a both temporally and spatially varying drift field induced by local winds/currents. The navigation problem is addressed assuming various information patterns about the drift field in the vicinity of the agent. In particular, three cases are considered, namely when the agent has complete information about the local drift, when the drift field is partially known, and when the drift field is completely unknown. By first establishing a duality between the navigation problem and a special class of problems of pursuit of a maneuvering target, several navigation schemes are presented, which are appropriately tailored to the fidelity of the information about the local drift available to the agent. The proposed navigation laws are dual to well-known pursuit strategies, such as pure pursuit, parallel guidance/navigation, line-of-sight guidance, motion camouflage, and pursuit with neutralization. Simulation results are presented to illustrate the theoretical developments
Flight Testing of Nap of-the-Earth Unmanned Helicopter Systems

(Georgia Institute of Technology, 2011-05) Johnson, Eric N. ; Mooney, John G. ; Ong, Chester ; Sahasrabudhe, Vineet ; Hartman, Jonathan

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time flight path optimization system to support high performance nap-of-the-Earth helicopter flight. The emphasis here is on optimization for a combination of low height above terrain/obstacles and high speeds. Multiple methods for generating the desired flight path were evaluated, including (1) a simple processing of each laser scan; and (2) a potential field based method. Simulation and flight test results have been obtained utilizing an onboard laser scanner to detect terrain and obstacles while flying at low altitude, and have successfully demonstrated obstacle avoidance in a realistic semi-urban environment at speeds up to 40 ft/s while maintaining a miss distance of 50 ft horizontally and vertically. These results indicate that the technical approach is sound, paving the way for testing of even lower altitudes, higher speeds, and more aggressive maneuvering in future work.