Unmanned helicopters in recent years have gained much attention due to their potential in both civil as well as military applications. Helicopter is an inherently unstable system. As a result there is a growing need of developing a control structure that allows the helicopter to perform various applications while remaining stable throughout the flight. This thesis presents developments of a robust controller for the vertical channel of an unmanned helicopter while carrying and dropping a payload. In addition, a simulation platform is developed in Simulink that uses a nonlinear six degree of freedom helicopter model. Quantitative Feedback Theory, a frequency domain technique, is used to design a controller that meets specific performance criteria when uncertainties associated with different payload weights exist in the system. The controller performance is examined in simulation for an Xcell 60 helicopter for effective lifting and dropping of up to 10 lb payload. The performance is then compared with a traditional Proportional-Integral-Derivative controller. Further, the effect of actuator dynamics on the controller performance is also evaluated. Finally, a controller that is robust in minimizing the effect of actuator dynamics and the payload drop while keeping the helicopter stable in flight is designed.

An autonomous system has been widely applied for various civil/military research because of its versatile capability of understanding high-level intent and direction of a surrounding environment and targets of interest. However, as autonomous systems can be out of control to cause serious loss, injury, or death in the worst case, the verification of their functionalities has got increasing attention. For that reason, this study is focused on the verification of a heterogeneous multi-agent autonomous system. The thesis first presents an overview of formal methods, especially focuses on model checking for autonomous systems verification. Then, six case studies are presented to verify the decision making behaviours of multi-agent system using two basic scenarios: surveillance and convoy. The initial system considered in the surveillance mission consists of a ground control system and a micro aerial vehicle. Their decision-making behaviours are represented by means of Kripke model and computational tree logic is used to specify the properties of this system. For automatic verification, MCMAS (Model Checker for Multi-Agent Systems) is adopted due to its novel capability to accommodate the multi-agent system. After that, the initial system is extended to include a substitute micro aerial vehicle. These initial case studies are then further extended based on SEAS DTC exemplar 2 dealing with behaviours of convoy protection. This case study includes now a ground control system, an unmanned aerial vehicle, and an unmanned ground vehicle. The MCMAS successfully verifies the targeting behaviours of the team-level unmanned systems. Reversely, these verification results help retrospectively improve the design of decision-making algorithms by considering additional agents and behaviours during four steps of scenario modification. Consequently, the last scenario deals with the system composed of a ground control system, two unmanned aerial vehicles, and four unmanned ground vehicles with fault-tolerant and communications relay capabilities. In conclusion, this study demonstrates the feasibility of model checking algorithms as a verification tool of a multi-agent system in an initial design stage. Moreover, this research can be an important first step of the certification of multi-agent autonomous systems for the domains of robotics, aerospace and aeronautics.

There is an on-going battle in the Department of Defense between reason and the faith in technology. Those ascribing to technological fundamentalism are blind to the empirical evidence that their faith in technology is obscuring the technological limitations that are evident. The desire for information dominance to reach the state of total transparency of the opponent in order to win the war is untenable. The reasoning voiced by skeptics should be heeded but the technological fundamentalists are deaf to their views. The use of UAVs have provided for limited visibility of the opponent and not the perfect Panopticon as envisioned. / Master of Public and International Affairs

We propose a new dynamically feasible trajectory generation algorithm that incorporates sternward motion for unmanned surface vehicles. This work is motivated by riverine applications where the operating environment is large and poorly known. We extend a navigation approach for forward path planning into a more versatile framework that includes safe and dynamically feasible backward trajectories. We pose the backward trajectory generation problem as a finite-horizon optimal control problem and transform it into a nonlinear programming problem by utilizing the direct shooting method. The nonlinear programming problem is solved using the Hooke-Jeeves numerical algorithm. We provide successful simulation and field-trial results that demonstrate the performance of backward path planning algorithm. / Master of Science

A system is designed to deploy and support a tethered ground robot from an autonomous helicopter. A winch is designed and built. Electrical hardware for power distribution and control are designed. Several applied controls problems are investigated. A control architecture is established and low level controllers are designed to meet the demands of two higher level algorithms. A tether tension controller is designed to avoid the danger of excess slack in the tether interfering with the robot's mobility. A payload sway damping controller is investigated and simulated. Its is shown to be effective in damping dangerous payload oscillations by modulating the vertical manipulation of the winch during hoisting. Future design recommendations are given regarding improvements for a second design iteration. / Master of Science

Published Article / Today's industries are based on an automated workplace. These automated workplaces are efficient, reconfigurable and intelligent automated environments. They are filled with technology, robotics, Automated Guided Vehicle (AGV) and, or Unmanned Aerial Vehicles (UAV) etc. For full automation will one need to effectively track an object, unmanned aerial vehicle (UAV) or automated guided vehicle (AGV). Effective tracking of vehicles can be used for control. This could result in less hardware on the craft that leads to a longer battery life, a bigger pay load or more processing power. This system track by using a stationary colour camera placed at an optimal placing in the automated workplace. The vehicle or objects are painted in two colours (colour A and colour B) that are not present in the automated workplace. The images from the camera are hue colour filtered to extract only the object or vehicle. The area, placement in frame and relationship between colour A and B are used for position and determine the orientation of AGV, UAV or object.

No description available.

This thesis work was undertaken to develop a new high level command for an unmanned aerial vehicle. The command is assumed to make the UAV follow a reference position that is placed on a certain distance to an object. At the same time the UAV is assumed to move more smoothly than the reference position and the UAV is allowed to follow the reference position with margin. The problem was solved with an automatic control system that takes the reference position as input signal and has a fictitious position as output signal. The fictitious position moves smoothly inside the margin and irregular behavior of the reference position is smoothed out by the automatic control system. The fictitious position is affected by strong feedback outside the margin and weak feedback inside the margin. This makes the fictitious position to stay inside the margin and moves smoothly inside the margin. The UAV follows the fictitious position instead of the reference position. In this way the UAV holds a certain distance to an object and at the same time moves smoother than the object.

Unmanned Aerial Vehicles (UAVs) are being used more frequently in surveillance scenarios for both civilian and military applications. One such application addresses a UAV patrolling a perimeter, where certain stations can receive alerts at random intervals. Once the UAV arrives at an alert site it can take two actions: 1. Loiter and gain information about the site. 2. Move on around the perimeter. The information that is gained is transmitted to an operator to allow him to classify the alert. The information is a function of the amount of time the UAV is at the alert site, also called the dwell time, and the maximum delay. The goal of the optimization is to classify the alert so as to maximize the expected discounted information gained by the UAV's actions at a station about an alert. This optimization problem can be readily solved using Dynamic Programming. Even though this approach generates feasible solutions, there are reasons to experiment with different approaches. A complication for Dynamic Programming arises when the perimeter patrol problem is expanded. This is that the number of states increases rapidly when one adds additional stations, nodes, or UAVs to the perimeter. This in effect greatly increases the computation time making the determination of the solution intractable. The following attempts to alleviate this problem by implementing a Reinforcement Learning technique to obtain the optimal solution, more specifically Q-Learning. Reinforcement Learning is a simulation-based version of Dynamic Programming and requires lesser information to compute sub-optimal solutions. The effectiveness of the policies generated using Reinforcement Learning for the perimeter patrol problem have been corroborated numerically in this thesis.

To satisfy the requirements of small satellites with attitude control requirements, a guidance, navigation, and control module was designed at the Texas Spacecraft Laboratory. However, these small satellites tend to have attitude constraints in the form of keep-in and keep-out cones. Two methods for autonomous constrained attitude guidance are presented. The first satisfies the problem of guiding a single axis through keep-out constraints while satisfying a second keep-in constraint through an adjoined optimization routine. The method leverages methods for discretizing the attitude shell combined with the graph pathfinding algorithm A*. The second approach generalizes the problem to any number of attitude constraints in the body and inertial frame by discretizing the quaternion space using a series of concentric discretized shells. An approach for discretized attitude optimization is created to allow the vehicle to identify which attitude mode to operate under while simultaneously optimizing the solar power input. The discretized constrained attitude guidance and attitude optimization techniques are tied together in the flight software architecture and tested in a hardware-in-the-loop simulation environment. / text