Dongwon Jung

Modelling and Hardware-in-the-Loop Simulation for a Small Unmanned Aerial Vehicle

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A 3-DoF Experimental Test-Bed for Integrated Attitude Dynamics and Control Research

This article presents the details of a newly constructed 3-dof experimental spacecraft simulator facility at the School of Aerospace Engineering at the Georgia Institute of Technology. The main component of the facility is a cylindrical platform located on a hemi-spherical air bearing that allows friction-free rotation about three axes. The facility includes a variety of actuators and sensors: gas thrusters, variable-speed controlled momentum gyros (which can operate solely in a reaction wheel (RW) or in a control momentum gyro (CMG) mode), a two-axial sun sensor, a high-precision three-axial rate gyro, a three-axial magnetometer, and a complementary inertial measurement unit. The facility offers a truly integrated attitude control system (IACS) for experimental testing of advanced attitude determination and control algorithms.

On-Line Path Generation for Unmanned Aerial Vehicles Using B-Spline Path Templates

The problem of generating a smooth reference path, given a finite family of discrete, locally optimal paths, is investigated. A finite discretization of the environment results in a sequence of obstacle-free square cells. The generated path must lie inside the channel generated by these obstacle-free cells, while minimizing certain performance criteria. Two constrained optimization problems are formulated and solved subject to the given geometric (linear) constraints and boundary conditions in order to generate a library of B-spline path templates offline. These templates are recalled during implementation and are merged together on the fly in order to construct a smooth and feasible reference path to be followed by a closed-loop tracking controller. Combined with a discrete path planner, the proposed algorithm provides a complete solution to the obstacle-free path-generation problem for an unmanned aerial vehicle in a computationally efficient manner, which is suitable for real-time implementation.

Inertial Attitude and Position Reference System Development for a Small UAV

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Real-time Implementation and Validation of a New Hierarchical Path Planning Scheme of UAVs via Hardware-in-the-Loop Simulation

We present a real-time hardware-in-the-loop simulation environment for the validation of a new hierarchical path planning and control algorithm for a small fixed-wing unmanned aerial vehicle (UAV). The complete control algorithm is validated through on-board, real-time implementation on a small autopilot having limited computational resources. We present two distinct real-time software frameworks for implementing the overall control architecture, including path planning, path smoothing, and path following. We emphasize, in particular, the use of a real-time kernel, which is shown to be an effective and robust way to accomplish real-time operation of small UAVs under non-trivial scenarios. By seamless integration of the whole control hierarchy using the real-time kernel, we demonstrate the soundness of the approach. The UAV equipped with a small autopilot, despite its limited computational resources, manages to accomplish sophisticated unsupervised navigation to the target, while autonomously avoiding obstacles.

Design and Development of a Low-Cost Test-Bed for Undergraduate Education in UAVs

This article describes the efforts undertaken at the School of Aerospace Engineering at the Georgia Institute of Technology for the development of a low-cost Unmanned Aerial Vehicle (UAV) test-bed for educational purposes. The objective of this test-bed is to provide an avenue for the involvement of undergraduate students (primarily) and graduate students (secondarily) in UAV research. The complete design and development of all hardware interfaces of the UAV platform including the on-board autopilot is presented. Based on flight test data a linear model has been developed for the lateral and longitudinal dynamics.

Bank-to-Turn Control for a Small UAV using Backstepping and Parameter Adaptation

Abstract In this research we consider the problem of path following control for a small fixed-wing unmanned aerial vehicle (UAV). Assuming the UAV is equipped with an autopilot for low level control, we adopt a kinematic error model with respect to the moving Serret-Frenet frame attached to a path for tracking controller design. A kinematic path following control law that commands heading rate is presented. Backstepping is applied to derive the roll angle command by taking into account the approximate closed-loop roll dynamics. A parameter adaptation technique is employed to account for the inaccurate time constant of the closed-loop roll dynamics during actual implementation. The path following control algorithm is validated in real-time through a high-fidelity hardware-in-the-loop simulation (HILS) environment showing the applicability of the algorithm on a real system.

Multiresolution on-line path planning for small unmanned aerial vehicles

In this article we propose a new online multiresolution path planning algorithm for a small unmanned air vehicle (UAV) with limited on-board computational resources. The proposed approach assumes that the UAV has detailed information of the environment only in the vicinity of its current position. Information about far away obstacles is also available, albeit with less accuracy. The proposed algorithm uses an integer arithmetic implementation of the fast lifting wavelet transform (FLWT) to get a multiresolution cell decomposition of the environment, whose dimension is commensurate to the onboard computational resources. A topological graph representation of the multiresolution cell decomposition is constructed efficiently, directly from the approximation and detail wavelet coefficients. Hardware-in-the-loop simulation (HILS) results validate the applicability of the algorithm on a small UAV autopilot. Comparisons with the standard D*-lite algorithm are also presented.

An Experimental Comparison of CMG Steering Control Laws

Control moment gyros (CMGs) are spacecraft attitude control actuators which act as torque amplifiers. They are thus suitable for attitude hold and reorientation of large spacecraft or for slew maneuvering. They provide the necessary torques via gimballing a spinning flywheel. A major problem encountered with the use of CMGs in practice is the possibility of singularities for certain combinations of gimbal angles. In such singular gimbal angle configurations the CMG cluster cannot generate torques along a certain direction. Several singularity avoidance and escape steering logics have been reported in the literature to solve the CMG singularity problem. In this paper, we experimentally compare three of the most common CMG steering logics using a realistic spacecraft simulator. We compare the relative merits of these steering laws with respect to their singularity avoidance capabilities and their efficiency in generating the commanded control torques. An adaptive feedback stabilizing control law is also used in conjunction with each CMG steering law to account for the gravity disturbance torque.

Multiresolution Hierarchical Path-Planning for Small UAVs Using Wavelet Decompositions

We present an algorithm for solving the shortest (collision-free) path planning problem for an agent (e.g., a small UAV) with limited on-board computational resources. The agent has detailed knowledge of the environment and the obstacles only in the vicinity of its current position. Far away obstacles are only partially known and may even change dynamically. The algorithm makes use of the wavelet transform to construct an approximation of the environment at different levels of resolution. We associate with this multiresolution representation of the environment a graph, whose dimension can be made commensurate to the on-board computational resources of the agent. The adjacency list of the graph can be efficiently constructed directly from the approximation and detail wavelet coefficients, thus further speeding up the whole process. Simulations are presented to test the efficiency of the algorithm using non-trivial scenarios.