Small unmanned aircraft systems (sUAS) will become a disruptive force on the modern battlefield. In recent years, sUAS size and cost have decreased while their capability has increased. They have forced a reconsideration of the air superiority paradigm held since the First World War. Perhaps their most attractive, and worrisome, feature is the huge range of combat roles that they might fulfill. The presence of sUAS on future battlefields is certain, but the role they will play and their impact on those battlefields are not. This work presents a decision support framework for sUAS deployment in small infantry units. The framework is designed to explore and evaluate multiple sUAS-small-unit deployment concepts' impact on small unit effectiveness in a combat scenario of interest. The framework helps decision makers identify high-level sUAS deployment principles for testing and validation in physical experiments before sUAS are implemented on the battlefield. The decision support framework comprises the following: 1) a definition of the sUAS-small-unit deployment concept design space and combat scenario, 2) an agent-based computer model for exploring sUAS deployment concepts, 3) a set of analysis tools for evaluating sUAS deployment impact on combat effectiveness, and 4) suggestions for synthesizing high-level sUAS deployment principles from the analysis. In this work, the decision support framework for sUAS-small-unit deployment is used to explore and evaluate the impact of deploying an infantry platoon with between one and nine unmanned aerial vehicles (UAV) operating in a reconnaissance role while executing one of several sUAS patrol pattern variants. In a scenario in which a defending platoon uses sUAS to intercept and aid in indirect fires targeting against a platoon of attacking infantry, the sUAS were shown to markedly improve the defending platoon's combat effectiveness. The framework is used to synthesize several key principles for sUAS deployment in the scenario. It shows that, when fewer UAVs are deployed, short-range sUAS patrols improve defender combat effectiveness. Conversely, when more UAVs are deployed, long-range sUAS patrols improve the defenders' ability to target attacking units with indirect fires, increasing the firepower concentrated against opponents. The analysis also shows that increasing the number of deployed UAVs improves the likelihood of defending warfighters surviving the engagement and the defenders' ability to detect and engage the attackers with indirect fires. Finally, the framework shows that sUAS can force alterations in attacker behavior, removing them from combat by non-violent, but highly effective, means.

A finite element model was developed for a range of human head-sUAS impacts to provide multiple case scenarios of impact severity at two response regions of interest: global and local. The hypothesis was that for certain impact scenarios, local response injuries of the brain (frontal, parietal, occipital, temporal lobes, and cerebellum) have a higher severity level compared to global response injury, the response at the Center of Gravity (CG) of the head. This study is the first one to predict and quantify the influence of impact parameters such as impact velocity, location, offset, and angle of impact to severity of injury. The findings show that an sUAS has the potential of causing minimal harm under certain impact scenarios, while other scenarios cause fatal injuries. Additionally, results indicate that the human head’s global response as a less viable response region of interest when measuring injury severity for clinical diagnosis. It is hoped that the results from this research can be useful to assist decision making for treatments and may offer different perspectives in sUAS designs or operation environments.

Small unmanned aerial vehicles (sUAV) can produce valuable data for inspections, topography, mapping, and 3D modeling of structures. Used by multiple industries, sUAV can help inspect and study geographic and structural sites. Typically, the sUAV and camera specifications require optimal conditions with known geography and fly pre-determined flight paths. However, if the environment changes, new undetectable aerial hazards may intersect new flight paths. This makes it difficult to construct autonomous flight path missions that are safe in post-hazard areas where the flight paths are based on previously built models or previously known terrain details. The goal of this research is to make it possible for an unskilled pilot to obtain high quality images at key angles which will facilitate the inspections of dangerous environments affected by natural disasters through the construction of accurate 3D models. An iterative process with converging variables can circumvent the current deficit in flying UAVs autonomously and make it possible for an unskilled pilot to gather high quality data for the construction of photogrammetric models. This can be achieved by gaining preliminary photogrammetric data, then creating new flight paths which consider new developments contained in the generated dense clouds. Initial flight paths are used to develop a coarse representation of the target area by aligning key tie points of the initial set of images. With each iteration, a 3D mesh is used to compute a new optimized view and flight path used for the data collection of a better-known location. These data are collected, the model updated, and a new flight path is computed until the model resolution meets the required heights or ground sample distances (GSD). This research uses basic UAVs and camera sensors to lower costs and reduce the need for specialized sensors or data analysis. The four basic stages followed in the study include: determination of required height reductions for comparison and convergent limitation, construction of real-time reconnaissance models, optimized view and flight paths with vertical and horizontal buffers constructed from previous models, and develop an autonomous process that combines the previous stages iteratively. This study advances the use of autonomous sUAV inspections by developing an iterative process of flying a sUAV to potentially detect and avoid buildings, trees, wires, and other hazards in an iterative manner with minimal pilot experience or human intervention; while optimally collecting the required images to generate geometric models of predetermined quality.

Emergency landing is a critical safety mechanism for aerial vehicles. Commercial aircraft have triply-redundant systems that greatly increase the probability that the pilot will be able to land the aircraft at a designated airfield in the event of an emergency. In general aviation, the chances of always reaching a designated airfield are lower, but the successful pilot might use landmarks and other visual information to safely land in unprepared locations. For small unmanned aircraft systems (sUAS), triply- or even doubly-redundant systems are unlikely due to size, weight, and power constraints. Additionally, there is a growing demand for beyond visual line of sight (BVLOS) operations, where an sUAS operator would be unable to guide the vehicle safely to the ground. This thesis presents a machine vision-based approach to emergency landing for small unmanned aircraft systems. In the event of an emergency, the vehicle uses a pre-compiled database of potential landing sites to select the most accessible location to land based on vehicle health. Because it is impossible to know the current state of any ground environment, a camera is used for real-time visual feedback. Using the recently developed Recursive-RANSAC algorithm, an arbitrary number of moving ground obstacles can be visually detected and tracked. If obstacles are present in the selected ditch site, the emergency landing system chooses a new ditch site to mitigate risk. This system is called Safe2Ditch.

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