Organizational Unit:

Unmanned Aerial Vehicle Research Facility

Permanent Link

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 27

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.
Monocular Visual Mapping for Obstacle Avoidance on UAVs

(Georgia Institute of Technology, 2014-01) Magree, Daniel ; Mooney, John G. ; Johnson, Eric N.

An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm.
Speed Profile Optimization for Optimal Path Tracking

(Georgia Institute of Technology, 2013-11) Zhao, Yiming ; Tsiotras, Panagiotis

In this paper, we study the problem of minimum-time, and minimum-energy speed profile optimization along a given path, which is a key step for solving the optimal path tracking problems for a particular class of dynamical systems. We focus on characterizing the optimal switching structure between extremal controls using optimal control theory, and present semi-analytical solutions to both problems. It is shown that the optimal solutions of these two problems are closely related.
Simultaneous Position and Attitude Control Without Linear and Angular Velocity Feedback Using Dual Quaternions

(Georgia Institute of Technology, 2013-06) Filipe, Nuno ; Tsiotras, Panagiotis

In this paper, we suggest a new representation for the combined translational and rotational dynamic equations of motion of a rigid body in terms of dual quaternions. We show that with this representation it is relatively straightforward to extend existing attitude controllers based on quaternions to combined position and attitude controllers based on dual quaternions. We show this by developing setpoint nonlinear controllers for the position and attitude of a rigid body with and without linear and angular velocity feedback based on existing attitude-only controllers with and without angular velocity feedback. The combined position and attitude velocity-free controller exploits the passivity of the rigid body dynamics and can be used when no linear and angular velocity measurements are available.
Speed Profile Optimization for Optimal Path Tracking

(Georgia Institute of Technology, 2013-06) Zhao, Yiming ; Tsiotras, Panagiotis

In this paper, we study the problem of minimum- time, and minimum-energy speed profile optimization along a given path, which is a key step for solving the optimal path tracking problems for a particular class of dynamical systems. We focus on characterizing the optimal switching structure between extremal controls using optimal control theory, and present semi-analytical solutions to both problems. It is shown that the optimal solutions of these two problems are closely related.
Rigid body motion tracking without linear and angular velocity feedback using dual quaternions

(Georgia Institute of Technology, 2013-06) Filipe, Nuno ; Tsiotras, Panagiotis

This paper takes advantage of a new, recently proposed representation of the combined translational and rotational dynamic equations of motion of a rigid body in terms of dual quaternions. We show that combined position and attitude tracking controllers based on dual quaternions can be developed with relatively low effort from existing attitude-only tracking controllers based on quaternions. We show this by developing an almost globally asymptotically stable nonlinear controller capable of simultaneously following time-varying position and attitude profiles without linear and angular velocity feedback based on an existing attitude-only tracking controller without angular velocity feedback.
Time-Optimal Vehicle Posture Control to Mitigate Unavoidable Collisions Using Conventional Control Inputs

(Georgia Institute of Technology, 2013-06) Chakraborty, Imon ; Tsiotras, Panagiotis ; Sanz-Diaz, Ricardo

This paper analyzes the mitigation of an unavoidable T-bone collision, where an “intelligent” vehicle executes an aggressive time-optimal rotation to achieve a favorable relative orientation with another vehicle prior to impact. The current paper extends the previous work by the authors on this problem, by modeling additional vehicle dynamics (neglected in the prior work) and by utilizing conventionally available control commands (that is, steering, braking, handbrake) for the maneuvering vehicle. The commands can either be applied directly by a trained driver, or (as in the majority of cases) can be executed with the help of a combination of an Active Front Steering (AFS) and an Electronic Stability Control (ESC) system onboard the vehicle. The optimal yaw rotation maneuver is analyzed for different initial speeds on both dry and wet asphalt. The results confirm the existence of an “option zone” for some cases, within which such an aggressive maneuver may be possible and perhaps even preferable to straight line braking.
Optimal Motion Planning with the Half-Car Dynamical Model for Autonomous High-Speed Driving

(Georgia Institute of Technology, 2013-06) Cowlagi, Raghvendra V. ; Peters, Steven C. ; Karaman, Sertac ; Frazzoli, Emilio ; Tsiotras, Panagiotis ; Iagnemma, Karl ; Jeong Hwan, Jeon

We discuss an implementation of the RRT* optimal motion planning algorithm for the half-car dynamical model to enable autonomous high-speed driving. To develop fast solutions of the associated local steering problem, we observe that the motion of a special point (namely, the front center of oscillation) can be modeled as a double integrator augmented with fictitious inputs. We first map the constraints on tire friction forces to constraints on these augmented inputs, which provides instantaneous, state-dependent bounds on the curvature of geometric paths feasibly traversable by the front center of oscillation. Next, we map the vehicle’s actual inputs to the augmented inputs. The local steering problem for the half- car dynamical model can then be transformed to a simpler steering problem for the front center of oscillation, which we solve efficiently by first constructing a curvature-bounded geometric path and then imposing a suitable speed profile on this geometric path. Finally, we demonstrate the efficacy of the proposed motion planner via numerical simulation results.
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.
Use of relaxation methods in sampling-based algorithms for optimal motion planning

(Georgia Institute of Technology, 2013-05) Arslan, Oktay ; Tsiotras, Panagiotis

everal variants of incremental sampling-based algorithms have been recently proposed in order to optimally solve motion planning problems. Popular examples include the RRT* and the PRM* algorithms. These algorithms are asymptotically optimal and thus provide high quality solutions. However, the convergence rate to the optimal solution may still be slow. Borrowing from ideas used in the well-known LPA* algorithm, in this paper we present a new incremental sampling-based motion planning algorithm based on Rapidly-exploring Random Graphs (RRG), denoted by RRT# (RRT “sharp”), which also guarantees asymptotic optimality, but, in addition, it also ensures that the constructed spanning tree rooted at the initial state contains lowest-cost path information for vertices which have the potential to be part of the optimal solution. This implies that the best possible solution is readily computed if there are some vertices in the current graph that are already in the goal region.