This thesis presents an integrated framework for online obstacle avoidance of rotary-wing unmanned aerial vehicles (UAVs), which can provide UAVs an obstacle field navigation capability in a partially or completely unknown obstacle-rich environment. The framework is composed of a LIDAR interface, a local obstacle grid generation, a receding horizon (RH) trajectory optimizer, a global shortest path search algorithm, and a climb rate limit detection logic. The key feature of the framework is the use of an optimization-based trajectory generation in which the obstacle avoidance problem is formulated as a nonlinear trajectory optimization problem with state and input constraints over the finite range of the sensor. This local trajectory optimization is combined with a global path search algorithm which provides a useful initial guess to the nonlinear optimization solver. Optimization is the natural process of finding the best trajectory that is dynamically feasible, safe within the vehicle's flight envelope, and collision-free at the same time. The optimal trajectory is continuously updated in real time by the numerical optimization solver, Nonlinear Trajectory Generation (NTG), which is a direct solver based on the spline approximation of trajectory for dynamically flat systems. In fact, the overall approach of this thesis to finding the optimal trajectory is similar to the model predictive control (MPC) or the receding horizon control (RHC), except that this thesis followed a two-layer design; thus, the optimal solution works as a guidance command to be followed by the controller of the vehicle. The framework is implemented in a real-time simulation environment, the Georgia Tech UAV Simulation Tool (GUST), and integrated in the onboard software of the rotary-wing UAV test-bed at Georgia Tech. Initially, the 2D vertical avoidance capability of real obstacles was tested in flight. Then the flight test evaluations were extended to the benchmark tests for 3D avoidance capability over the virtual obstacles, and finally it was demonstrated on real obstacles located at the McKenna MOUT site in Fort Benning, Georgia. Simulations and flight test evaluations demonstrate the feasibility of the developed framework for UAV applications involving low-altitude flight in an urban area.

To address the concern of classical guidance and control designs (where guidance and control loops are designed separately in an “outer loop – inner loop” structure), integrated guidance and control (IGC) ideas have been proposed in the recent literature. An important limitation of the existing IGC algorithms, however, is that they do not explicitly exploit the inherent time scale separation that exist in aerospace vehicles between rotational and translational motions, and hence, can be ineffective unless the engagement geometry is close to the collision triangle. To address this, a time scale separated partial integrated guidance and control (PIGC) structure has been proposed in this thesis. In this two-loop design, the commanded pitch and yaw rates are directly generated from an outer loop optimal control formulation, which is solved in a computationally efficient manner using the recently-developed model predictive static programming (MPSP) and Model Predictive Spread Control (MPSC) techniques. The necessary roll-rate command is generated from a roll-stabilization loop. The inner loop then tracks the outer loop commands using the nonlinear dynamic inversion philosophy. However, unlike classical guidance and control designs, in both the loops the Six-DOF interceptor model is used directly. This intelligent manipulation preserves the inherent time scale separation property between the translational and rotational dynamics, and hence overcomes the deficiency of current IGC designs, while preserving the benefits of the IGC philosophy. The new approach has been applied in the terminal phase of an endo-atmospheric interceptor for engaging incoming high speed ballistic missile targets. Six–DOF simulation results will be presented accounting for a 3-D engagement geometry to demonstrate the usefulness of this method. It offers two important advantages: (i) it leads to very small (near-zero) miss distance, resulting in a “hit-to-kill” scenario and (ii) it also leads to lesser and smoother body-rate demands, relaxing the demand on actuators as well as enlarging the ‘capture region’ (which relaxes the demand on mid-course guidance). Next, to address the problem of modeling inaccuracy that is inherent in aerospace vehicles (mainly because of the inaccuracy of aerodynamic model generated from wind-tunnel testing), a neuro-adaptive design is augmented to dynamic inversion technique in the inner loop. In this design the unmodelled dynamics is adaptively captured using three neural networks in the roll, pitch and yaw channels. Training of the neural networks is carried out online using the Lyapunov stability theory, which results in stability of the inner-loop error dynamics as well as boundedness of network weights. This adaptive body rate tracking loop augmented with the sub-optimal feedback guidance loop results in substantial enhancement of interception performance in presence of realistic (i.e. fairly large) modeling uncertainties of the interceptor. The results have also been validated with representative seeker noise.

Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning. As a baseline system that estimates ownship states, an integrated navigation system is designed by using an extended Kalman filter (EKF) with sequential measurement updates. In order to effectively fuse various sources of aiding sensor information, the sequential measurement update algorithm is introduced in the design of the integrated navigation system with the objective of being implemented in low-cost autonomous UAV's. Since estimated state accuracy using a low-cost, MEMS-based IMU degrades with time, several absolute (low update rate but bounded error in time) sensors, including the GPS receiver, the magnetometer, and the altimeter, can compensate for time-degrading errors. In this work, the sequential measurement update algorithm in smaller vectors and matrices is capable of providing a convenient framework for fusing the many sources of information in the design of integrated navigation systems. In this framework, several aiding sensor measurements with different size and update rates are easily fused with basic high-rate IMU processing. In order to provide a new mechanism that estimates ownship states, a new nonlinear filtering framework, called the unscented Kalman filter (UKF) with sequential measurement updates, is developed and applied to the design of a new integrated navigation system. The UKF is known to be more accurate and convenient to use with a slightly higher computational cost. This filter provides at least second-order accuracy by approximating Gaussian distributions rather than arbitrary nonlinear functions. This is compared to the first-order accuracy of the well-known EKF based on linearization. In addition, the step of computing the often troublesome Jacobian matrices, always required in the design of an integrated navigation system using the EKF, is eliminated. Furthermore, by employing the concept of sequential measurement updates in the UKF, we can add the advantages of sequential measurement update strategy such as easy compensation of sensor latency, easy fusion of multi-sensors, and easy addition and subtraction of new sensors while maintaining those of the standard UKF such as accurate estimation and removal of Jacobian matrices. Simulation results show better performance of the UKF-based navigation system than the EKF-based system since the UKF-based system is more robust to initial accelerometer and rate gyro biases and more accurate in terms of reducing transient peaks and steady-state errors in ownship state estimation. In order to estimate target tracking states or target kinematics, a new vision-based tracking system is designed by using a UKF in the scenario of three-dimensional air-to-air tracking. The tracking system can estimate not only the target tracking states but also several target characteristics including target size and acceleration. By introducing the UKF, the new vision-based tracking system presents good estimation performance by overcoming the highly nonlinear characteristics of the problem with a relatively simplified formulation. Moreover, the computational step of messy Jacobian matrices involved in the target acceleration dynamics and angular measurements is removed. A new particle filtering framework, called an extended marginalized particle filter (EMPF), is developed and applied to the design of a new vision-based tracking system. In this work, only three position components with vision measurements are solved in particle filtering part by applying Rao-Blackwellization or marginalization approach, and the other dynamics, including the target nonlinear acceleration model, with Gaussian noise are effectively handled by using the UKF. Since vision information can be better represented by probabilistic measurements and the EMPF framework can be easily extended to handle this type of measurements, better performance in estimating target tracking states will be achieved by directly incorporating non-Gaussian, probabilistic vision information as the measurement inputs to the vision-based tracking system in the EMPF framework.

The advantages of UAVs in the aviation arena have led to extensive research activities on autonomous technology of UAVs to achieve specific mission objectives. This thesis mainly focuses on the development of a mission-based guidance system. Among various missions expected of UAVs for future needs, autonomous formation flight (AFF) and obstacle avoidance within safe operation limits are investigated. In the design of an adaptive guidance system for AFF, the leader information except position is assumed to be unknown to a follower. Thus, the only measured information related to the leader is the line-of-sight range and angle. Adding an adaptive element with neural networks into the guidance system provides a capability to effectively handle leader's velocity changes. Therefore, this method can be applied to the AFF control systems that use passive sensing methods. The simulation and flight test results clearly show that the adaptive guidance control system is a promising solution for autonomous formation flight of UAVs. The successful flight evaluations using the GTMax rotary wing UAV also demonstrate unique maneuvering aspects associated with rotary wing UAVs in formation flight. In the design of an autonomous obstacle avoidance system, an integrated approach is proposed to resolve the conflict between aggressive maneuvering needed for obstacle avoidance and the constrained maneuvering needed for envelope protection. A time-optimal problem with obstacle and envelope constraints is used for an integrated approach for obstacle avoidance and envelope protection. The Nonlinear trajectory generator (NTG) is used as a real-time optimization solver. The computational complexity arising from the obstacle constraints is reduced by converting the obstacle constraints into a safe waypoint constraint along with an implicit requirement that the horizontal velocity during the avoidance maneuver must be non-negative. The issue of when to initiate a time-optimal avoidance maneuver is addressed by including a requirement that the vehicle must maintain its original flight path to the maximum extent possible. The simulation results using a rotary wing UAV demonstrate the feasibility of the proposed approach for obstacle avoidance with envelope protection.

The field of guided projectile research has continually grown over the past several decades. Guided projectiles, typically encompassing bullets, mortars, and artillery shells, incorporate some sort of guidance and control mechanism to generate trajectory alterations. This serves to increase accuracy and decrease collateral damage. Control mechanisms for smart weapons must be able to withstand extreme acceleration loads at launch while remain simple for cost and reliability reasons. One type of control mechanism utilizes controllable internal translating masses (ITM's) that oscillate within the projectile to generate control forces. Several techniques for using internal translating masses for smart weapon flight control purposes are explored here. Specifically, the use of ITM's as direct control mechanisms, as a means to increase control authority, and as a means to protect the smart weapons sensor suite are examined. It is first shown that oscillating a mass orthogonal to the projectile axis of symmetry generates reasonable control force in statically-stable rounds. Trade studies examine the impact of mass size, mass offset from the center of gravity, and reductions in static stability on control authority. Then, the topic of static margin control through mass center modification is explored. This is accomplished by translating a mass in flight along the projectile axis of symmetry. Results show that this system allows for greater control authority and reduced throw-off error at launch. Another study, aimed at examining shock reduction potential at launch rather than static margin alteration, also considers ITM movement along the projectile centerline. In these studies, the ITM is comprised of sensitive electronic sensors, and is configured as a first-order damper during launch. Trade study results show that although the mechanism cannot substantially reduce the magnitude of launch loads, it is successful at dampening harmful structural vibrations typically experienced after muzzle exit. Finally, an active control system is developed for the ITM control mechanism using sliding mode methodology. Example cases and Monte Carlo simulations incorporating model uncertainties and sensor errors show that ITM control of projectiles can substantially reduce dispersion error. Furthermore, the novel sliding mode control law is shown to be highly robust to feedback disturbances. In a final study, combined ITM-canard control of projectiles is explored, concluding that ITM mechanisms can serve as a useful supplement in increasing the efficiency of currently-deployed control mechanisms.

The recent technological advances in the field of autonomous vehicles have resulted in a growing impetus for researchers to improve the current framework of mission planning and execution within both the military and civilian contexts. Many recent efforts towards this direction emphasize the importance of replacing the so-called monolithic paradigm, where a mission is planned, monitored, and controlled by a unique global decision maker, with a network centric paradigm, where the same mission related tasks are performed by networks of interacting decision makers (autonomous vehicles). The interest in applications involving teams of autonomous vehicles is expected to significantly grow in the near future as new paradigms for their use are constantly being proposed for a diverse spectrum of real world applications. One promising approach to extend available techniques for addressing problems involving a single autonomous vehicle to those involving teams of autonomous vehicles is to use the concept of Voronoi diagram as a means for reducing the complexity of the multi-vehicle problem. In particular, the Voronoi diagram provides a spatial partition of the environment the team of vehicles operate in, where each element of this partition is associated with a unique vehicle from the team. The partition induces, in turn, a graph abstraction of the operating space that is in a one-to-one correspondence with the network abstraction of the team of autonomous vehicles; a fact that can provide both conceptual and analytical advantages during mission planning and execution. In this dissertation, we propose the use of a new class of Voronoi-like partitioning schemes with respect to state-dependent proximity (pseudo-) metrics rather than the Euclidean distance or other generalized distance functions, which are typically used in the literature. An important nuance here is that, in contrast to the Euclidean distance, state-dependent metrics can succinctly capture system theoretic features of each vehicle from the team (e.g., vehicle kinematics), as well as the environment-vehicle interactions, which are induced, for example, by local winds/currents. We subsequently illustrate how the proposed concept of state-dependent Voronoi-like partition can induce local control schemes for problems involving networks of spatially distributed autonomous vehicles by examining different application scenarios.

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Johnson, Eric; Committee Co-Chair: Calise, Anthony; Committee Member: Prasad, J.V.R.; Committee Member: Tannenbaum, Allen; Committee Member: Tsiotras, Panagiotis.

The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.

The objective of this thesis is to design a relative navigation and guidance system for unmanned aerial vehicles (UAVs) for vision-based control applications. The vision-based navigation, guidance and control has been one of the most focused on research topics for the automation of UAVs. This is because in nature, birds and insects use vision as the exclusive sensor for object detection and navigation. In particular, this thesis studies the monocular vision-based navigation and guidance. Since 2-D vision-based measurements are nonlinear with respect to the 3-D relative states, an extended Kalman filter (EKF) is applied in the navigation system design. The EKF-based navigation system is integrated with a real-time image processing algorithm and is tested in simulations and flight tests. The first closed-loop vision-based formation flight has been achieved. In addition, vision-based 3-D terrain recovery was performed in simulations. A vision-based obstacle avoidance problem is specially addressed in this thesis. A navigation and guidance system is designed for a UAV to achieve a mission of waypoint tracking while avoiding unforeseen stationary obstacles by using vision information. A 3-D collision criterion is established by using a collision-cone approach. A minimum-effort guidance (MEG) law is applied for a guidance design, and it is shown that the control effort can be reduced by using the MEG-based guidance instead of a conventional guidance law. The system is evaluated in a 6 DoF flight simulation and also in a flight test. For monocular vision-based control problems, vision-based estimation performance highly depends on the relative motion of the vehicle with respect to the target. Therefore, this thesis aims to derive an optimal guidance law to achieve a given mission under the condition of using the EKF-based relative navigation. Stochastic optimization is formulated to minimize the expected cost including the guidance error and the control effort. A suboptimal guidance law is derived based on an idea of the one-step-ahead (OSA) optimization. Simulation results show that the suggested guidance law significantly improves the guidance performance. Furthermore, the OSA optimization is generalized as the n-step-ahead optimization for an arbitrary number of n, and their optimality and computational cost are investigated.