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 39

Vision-Based Attitude Determination Using A SLAM Algorithm During Relative Circumnavigation Of Non-cooperative Objects

(Georgia Institute of Technology, 2016-09) Antonello, Andrea ; Tsiotras, Panagiotis

We approach the problem of a chaser satellite circumnavigating a target object in a relative orbit. The objective is to obtain a map of the scenario and to measure the reciprocal position of the chaser-target pair, in order to subsequently perform proximity operations (active debris removal, rendezvous, servicing, etc.) more reliably. This work analyzes the case of a target-chaser scenario in a closed Clohessy-Wiltshire relative orbit. The chaser satellite has a vision sensor and observes a set of landmarks on the target satellite: the control acts on the yaw-rotation of the detector. By de ning a probability distribution over a set of feasible control trajectories, we perform a search for a near-optimal solution. At the core of this approach lies the cross entropy minimization technique for estimating rare-event probabilities, which iteratively approximates the sampling distribution towards regions of progressively lower cost until converging to the optimum. We present simulations of a tracking scenario, demonstrating the validity of the proposed control technique. Performance of the proposed policy is compared with the case of a non controlled sensor by evaluating the time spent under observation and the residual uncertainty bounds on the landmarks. Results con rm the validity of the proposed approach.
Performance Analysis of Three Cost Policies for the Control of a Camera in Relative Circumnavigation Scenarios

(Georgia Institute of Technology, 2016-09) Antonello, Andrea ; Carron, Andrea ; Carli, Ruggero ; Tsiotras, Panagiotis

In this paper, we address the relative navigation problem of a chaser circumnavigating a target. The chaser has an on-board camera and observes a set of features on the target; the goal is to obtain a detailed map of the landmarks. By controlling the yaw-rotation of the sensor it is possible to maximize the time allocated to landmark observation. An Extended Kalman Filter (EKF) provides state uncertainty information, which can then be used to design a cost function to be minimized by the optimal yaw controller. Three different cost functions are designed and simulated, and their performances are compared with the case of a xed, nadir-pointing camera. The analysis of localization uncertainties for different sets of initial conditions con rmed the superior performance of the proposed novel control methodology.
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.
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.
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.