Pseudo-spectral optimal solvers are used to optimize numerically a performance index of a dynamical system with differential constraints. Although these solvers are commonly used for space vehicles and space launchers for trajectory optimization, few experimental papers exist on optimal control of small airships. The objective of this thesis is to evaluate the use of a pseudo-spectral optimal control solver for generating dynamically constrained, minimal time trajectories. A dynamical model of a small airship is presented, with its experimental virtual mass, drag and motor experimentally modeled. The problems are solved in PSOPT, a pseudo-spectral optimal control code. Experimental tests with a small scale model are performed to evaluate the generated paths. Although drift occurs, as a consequence of an open loop control, the vehicle is capable of following the path. This results of this thesis may find uses in verifying how close to optimal discreet path planners are, to plan complex trajectories on short distances, or to generate dynamic maneuverer such as take-off or landing. Ultimately, improving path planning of small airships will improve their safety, maneuverability and flight-time, which makes them fit for scientific monitoring, for search and rescue, or as mobile telecommunications platforms.

In the event of a disaster, first responders must rapidly gain situational awareness about the environment in order to plan effective response operations. Unmanned ground vehicles are well suited for this task but often require a strong communication link to a remote ground station to effectively relay information. When considering an obstacle-rich environment, non-line-of-sight conditions and naive navigation strategies can cause substantial degradations in radio link quality. Therefore, this thesis incorporates an unmanned aerial vehicle as a radio repeating node and presents a path planning strategy to cooperatively navigate the vehicle team so that radio link health is maintained. This navigation technique is formulated as an A*-based search and this thesis presents the formulation of this path planner as well as an investigation into strategies that provide computational efficiency to the search process. The path planner uses predictions of radio signal health at different vehicle configurations to effectively navigate the vehicles and simulations have shown that the path planner produces favorable results in comparison to several conceivable naive radio repeating variants. The results also show that the radio repeating path planner has outperformed the naive variants in both simulated environments and in field testing where a Yamaha RMAX unmanned helicopter and a ground vehicle were used as the vehicle team. Since A* is a general search process, this thesis also presents a roadway detection algorithm using A* and edge detection image processing techniques. This algorithm can supplement unmanned vehicle operations and has shown favorable performance for images with well-defined roadways. / Master of Science

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In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on prior experience and subjective judgment. We propose a Bayesian model that utilizes publicly available terrain features data to help model lost-person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also make it possible to incorporate human-behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayes Chi-squared test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation of a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost-person's behaviors. Once a probability distribution map is in place, areas with higher probabilities are searched first in order to find the missing person in the shortest expected time. When using a Unmanned Aerial Vehicle (UAV) to support search, the onboard video camera should cover as much of the important areas as possible within a set time. We explore several algorithms (with and without set destination) and describe some novel techniques in solving this path-planning problem and compare their performances against typical WiSAR scenarios. This problem is NP-hard, but our algorithms yield high quality solutions that approximate the optimal solution, making efficient use of the limited UAV flying time. The capability of planning a path with a set destination also enables the UAV operator to plan a path strategically while letting the UAV plan the path locally.

Search operations can be described by the interaction between three entities: the target, the sensor, and the environment. Past treatments of the search problem have focused primarily on the interaction between the sensor and the target. The effects that the environment has on the target and sensor have been greatly simplified or ignored completely. The wilderness search and rescue scenario is one case in which these interactions cannot be safely ignored. Using the wilderness search and rescue problem as our motivating example, we develop an algorithm for planning search paths for a small unmanned aerial vehicle (UAV) over rough terrain environments that provide complete coverage of the specified terrain region while minimizing effort wasted on duplicate coverage. The major components of this algorithm include 1) breaking the search region into smaller sub-regions that are easier to deal with, and 2) planning the search for each of these sub-regions. The original contributions of this thesis focus on the latter of these two components. We use a method based on the directional offset of terrain contours to produce paths on the terrain for the sensor to observe as the UAV follows the flight path. We then employ directional-offset methods again by moving in the direction along the terrain normal from the sensor path to generate a flight path that lies in the air a specified distance away from the points on the terrain that are to be observed. These two paths are linked in a way that provides the sensor with an ample viewing opportunity of the terrain regions below. We implement this planning algorithm in software with Matlab, and provide a complete simulation of a UAV that follows the planned search pattern. Our planning algorithm produced search paths that were 94 to 100 percent complete in test scenarios for several rough-terrain regions. Missed regions for these plans were near the search boundaries, and coverage could easily be provided by subsequent plans. We recommend the study of region segmentation, with careful consideration of planning algorithms as the major focus of future work.

The work in this thesis is on two topics. The first topic focuses on collaboration between a UAV and a UGV to track a moving ground target. The second topic focuses on deriving a guidance law for a small-scale controllable projectile to be guided into a target. For the first topic, we implement a path planning algorithm in a hardware system for a UAV and UGV to track a ground target. The algorithm is designed for urban environments where it is common for objects to obstruct sensors located on the UAV and the UGV. During the hardware system's implementation, multiple problems prevented the hardware system from functioning properly. We will describe solutions to these problems. For the second topic, we develop a guidance law for a small-scale controllable projectile using Lyapunov analysis techniques. We implement a PID controller on the body-axes pitch rate and yaw rate of the projectile such that the behavior of the pitch rate and yaw rate can be approximated as a second order system. We derive inputs for the pitch rate and yaw rate using backstepping and adaptive control techniques. The guidance law we develop guarantees the rocket will point at its intended destination. Additionally, we present expressions for the kinematics and dynamics of the rocket's motion and define the forces and moments that act on the rocket's body.

Efforts to learn more about the oceans that surround us have increased dramatically as the technological ability to do so grows. Autonomous Underwater Vehicles (AUVs) are one such technological advance. They allow for rapid deployment and can gather data quickly in places and ways that traditional measurement systems (bouys, profilers, etc.) cannot. A ROMS-based data assimilation method was developed that intelligently plans for and integrates AUV measurements with the goal of minimizing model standard deviation. An algorithm developed for this system is first described that optimizes paths for AUVs that seeks to improve the model by gathering data in high-interest locations. This algorithm and its effect on the ocean model are tested by comparing the results of missions made with the algorithm and missions created by hand. The results of the experiments demonstrate that the system is successful in improving the ROMS ocean model. Also shown are results comparing optimized missions and unoptimized missions.

Every year, the ports of entry of the continental United States receive millions of containers from container ships for processing. These containers contain everything that the country imports, and sometimes regulated items can be hidden inside them in attempt to smuggle them illegally into the country. Some of these items may be radioactive material meant for criminal purposes and represent a threat to national security. The containers are currently being scanned for radioactivity as they leave the port, but before leaving the port, containers can sit inside the port for weeks. It can be beneficial to scan these containers before they are picked up to catch the illegal material sooner and reduce the risk of danger to those nearby. Uncrewed Aerial Systems can be useful for scanning container stacks in container fields since they can be attached with sensors and reach heights that are difficult for humans. They can also scan autonomously, requiring less over watch from people. This thesis attempts to solve the problem of autonomous search by using an initial 3D scan of the search area to input into a 3D path planning algorithm to generate a flight path that will sufficiently scan the search area while minimizing flight time. Coverage is a main area of concern, as well is computational complexity and time. In order to maintain security of the aircraft, the path must be generated on-board the aircraft, and as such use on-board, lightweight, computers. The approach taken in this thesis is by breaking the problem down into 2D layers, and then developing paths on each layer based on where the obstacles are. In order to maximize coverage, contours are generated around the obstacles. The vertices of the contours are then treated like points to visit in a Travelling Salesman Problem. To incentivize paths that run alongside the obstacles for better radiation detection, paths that do not run close to the obstacles are given a higher cost than those that do, resulting in a cost-minimizing path planning algorithm yielding paths that stay close to obstacles. The Travelling Salesman Problem algorithm then yields the most time effective path to cover the area while maintaining a distance healthy for radiation scanning from the obstacles. / Master of Science / Every year, the ports of entry of the continental United States receive millions of containers from container ships for processing. These containers contain everything that the country imports, and sometimes regulated items can be hidden inside them in attempt to smuggle them illegally into the country. Some of these items may be radioactive material meant for criminal purposes and represent a threat to national security. It can be beneficial to scan these containers before they are picked up to catch the illegal material sooner and reduce the risk of danger to those nearby. Uncrewed Aerial Systems can be useful for scanning container stacks in container fields since they can be attached with sensors and reach heights that are difficult for humans. They can also scan autonomously, requiring less over watch from people. This thesis attempts to solve the problem of autonomous search by using an initial 3D scan of the search area to input into a 3D path planning algorithm to sufficiently scan the search area while minimizing flight time.

Underwater robotic vehicles are used in a variety of environments that would be dangerous for humans. For these vehicles to be successful, they need to be tolerant of a variety of internal and external faults. To be resilient to internal faults, the system must be capable of determining the source of faulty behavior. However many different faults within a robotic vehicle can create identical faulty behavior, which makes the vehicles impossible to diagnose using conventional methods. I propose a novel active diagnosis method for differentiating between faults that would otherwise have identical behavior. I apply this method to a communication system and a power distribution system in a robotic vehicle and show that active diagnosis is successful in diagnosing partially observable faults. An example of an external fault is inter-robot communication in underwater robotics. The primary communication method for underwater vehicles is acoustic communication which relies heavily on line-of-sight tracking and range. This can cause severe packet loss between agents when a vehicle is operating around obstacles. I propose novel path-planning methods for an Autonomous Underwater Vehicle (AUV) that ferries messages between agents. I applied this method to a custom underwater simulator and illustrate how it can be used to preserve at least twice as many packets sent between agents than would be obtained using conventional methods.