Due to their small size, relative covertness, and high maneuverability, micro rotorcraft are ideal for a plethora of civilian and military applications in an urban environment such as, surveillance, monitoring, mapping, and search and rescue. It is envisioned that these vehicles will operate indoors confined complex spaces, and outside near the ground—among buildings and other obstacles. The aerodynamic velocity fields in these areas are notoriously complex with the mean winds varying spatially and temporally with sharp changes in wind magnitude and direction over small distances. This results in velocity perturbations which are on the same order of magnitude as the maximum flight speeds of micro rotorcraft leading to stall, large attitude perturbations, and loss of control; thus preventing micro rotorcraft from carrying out even the most basic missions. This dissertation starts to fill the void in the literature on this topic by assessing how to design a micro coaxial helicopter with improved gust response in complex urban environments. Both experimental flight tests and modeling and simulation tools are developed and executed to analytically understand the challenges and potential solutions to enable rotorcraft to operate efficiently and robustly in urban environments. A set of performance metrics were developed to provide a framework to assess mission-level performance of micro rotorcraft in both flight experiments and simulation trade studies. A high fidelity dynamic model of a coaxial helicopter was developed to accurately simulate vehicle response to urban wind disturbances. The model was validated using flight experiments in a motion capture facility. Additionally, a dynamic inversion based Gust Rejection Control architecture was developed for the dynamic simulation which included a novel wind estimation algorithm that was utilized to improve controller performance and create a flight envelope protection scheme. The high fidelity dynamic model was employed to perform a variety of trade studies to: analyze vehicle response to prototypical urban wind kernels, understand the affect of wind estimation on the control architecture, assess the level of model fidelity required to adequately simulate vehicle response to urban winds, and identify key platform design parameter trends to improve wind disturbance capabilities. Overall the results show the challenges micro rotorcraft face in urban environments while highlighting some trends that can be helpful for future design and analysis efforts.

Les véhicules autonomes sont des engins mobiles caractérisés par l’absence de pilote à leur bord et font partie d’un système plus global comprenant des éléments tels qu’une station de contrôle. Ils présentent la particularité d’avoir une conception spécifique liée à la mission assignée et peuvent être déployés dans des milieux divers et hétérogènes, incluant le milieu spatial, aérien,terrestre, marin de surface et sous-marin.Certaines missions requièrent la coopération de véhicules hétérogènes, où chaque type de véhicule réalise une mission locale pour permettre la réalisation d’une mission globale. La coopération entre les véhicules nécessite l’interopérabilité des communications. Même si des efforts ont été entrepris dans ce sens en normalisant les couches applicatives, ces travaux restent insuffisants.En effet, il n’existe pas de protocole qui assure l’acheminement des données entre différents types de véhicules qui possèdent une mobilité propre et utilisent parfois des médias de communication différents, comme les engins sous-marins et terrestres. L’objectif principal de cette thèse est de permettre à tous les engins de communiquer entre eux et de rendre cette interconnexion transparente. Pour cela, nous adoptons une approche multicouche qui nous permet de diffuser et d’acheminer des données vers n’importe quel engin. Il devient alors possible pour chaque véhicule de transmettre des données de manière transparente à un autre véhicule de nature différente sans connaître la topologie globale du réseau. Pour cela nous avons conçu un protocole de routage qui adapte sa politique en fonction du contexte et de l’environnement.Nous exploitons également un mode de diffusion qui permet de transmettre des données vers un engin faisant partie d’un groupe cible en nous basant sur leurs caractéristiques afin d’acheminer les données de manière optimale. / Unmanned vehicles are defined as autonomous entities with no operator on board. They are a part of a global system called Unmanned System which also includes elements such as a control station. These vehicles are designed to fulfil the requirements of assigned missions and can be deployed in spatial, aerial, terrestrial and maritime environments. Since a mission cannot be accomplished with a single vehicle, vehicles have to cooperate in order to achieve a global mission. However, cooperation requires communication interoperability between all vehicles. Even if previous works have standardized application protocols, it is not sufficient to ensure data delivery between all vehicles, since they have a specific mobility pattern and sometimes different network interfaces. The main goal of this thesis is to offer a seamless network, including all kinds of unmanned systems. We propose a cross layer approach in order to route and deliver data to any vehicle. In this context, each vehicle is able to transmit data to another without information on the global topology. We have developed a routing protocol, which adapts its strategy, according to the contextand to the network environment. In addition, we exploit the any cast diffusion technique based on vehicles features in order to adopt an optimal routing scheme.

Nitrogen run-off from agriculture have been linked to human health problems on a global level. Large-scale conventional producers struggle to redefine themselves as sustainable because reducing nitrogen (N) inputs without justification or validation may lead to severe profit losses. Small unmanned aerial systems (sUAS) sensing may allow for decreased N runoff. Failure to address this problem will exacerbate already excessive N runoff into the Mississippi River and beyond. The purpose of this study was to reduce fertilizer N input using sUAS technology to assess crop canopy needs. In 2020 and 2021, variable rate nitrogen (VRN) side-dress N application maps were calculated on early corn and cotton crops sensed with MicaSense® technology. The SCCCI and FENDVI VIs most often were highly related by SEq to early corn and cotton canopy N status. VariRite™ technology was successfully implemented in producer’s fields using VI calibrated imagery captured from sUAS.

When working with a complex AI or robotics system in a specific application, users often need to incorporate their special domain knowledge into the autonomous system. Such needs call for the ability to manage autonomy. However, managing autonomy can be a difficult task because the internal mechanisms and algorithms of the autonomous components may be beyond the users' understanding. We propose an approach where users manage autonomy indirectly by managing information provided to the intelligent system hierarchically at three different temporal scales: strategic, between-episodes, and within-episode. Information management tools at multiple temporal scales allow users to influence the autonomous behaviors of the system without the need for tedious direct/manual control. Information fed to the system can be in the forms of areas of focus, representations of task difficulty, and the amount of autonomy desired. We apply this approach to using an Unmanned Aerial Vehicle (UAV) to support Wilderness Search and Rescue (WiSAR). This dissertation presents autonomous algorithms/components and autonomy management tools/interfaces we designed at different temporal scales, and provides evidence that the approach improves the performance of the human-robot team and the experience of the human partner.

The U.S. Army desires to improve safety during Intelligence, Surveillance, Reconnaissance (ISR) operations by removing Warfighters from direct line-of-fire by enhancing ISR operational capabilities with unmanned systems, also known as Robot-Aided ISR (RAISR) (DOD, 2013). Additionally, RAISR presents an opportunity to fulfill ISR capability requirements of modern combat environments including: detection of High-Value Individuals (HVI) from safer distances, identification of baseline behavior, and interpretation of adversarial intent (U.S. Army, 2008). Along with the demand and projected acquisition of RAISR technology, there is the added need to design training requirements for system operation and task execution instruction. While documentation identifying specific training standards and objectives for ISR tasks utilizing unmanned systems is limited (DOD, 2013), simulation-based training has been identified as a critical training medium for RAISR (U.S. Army, 2008). ISR analysts will primarily conduct RAISR tasks via Indirect Vision Displays (IVD) which transition well into multimodal simulations (Salcedo, Lackey, & Maraj, 2014). However, simulation alone may not fulfill the complex training needs of RAISR tasks, therefore, incorporating instructional support may improve the effectiveness of training (Oser, Gualtieri, Cannon-Bowers, & Salas, 1999). One method to accomplish this is to utilize a Scenario-Based Training (SBT) framework enhanced with instructional strategies to target specific training objectives. The purpose for the present experiment was to assess the effectiveness of SBT enhanced with selected instructional strategies for a PC-based RAISR training simulation. The specific task type was the identification of HVIs within a group through behavior cue analysis. The instructional strategies assessed in this experiment, Highlighting and Massed Exposure, have shown to improve attentional weighting, visual search, and pattern recognition skills, which are critical for successful behavior cue analysis. Training effectiveness was evaluated by analyzing the impact of the instructional strategies on performance outcomes, including detection accuracy, classification accuracy, and median response time, and perceptions of the level of engagement, immersion, and presence during training exercises. Performance results revealed that the Massed Exposure strategy produced significantly faster response times for one subtle and one familiar target behavior cue. Perception results indicated that Highlighting was the least challenging instructional strategy and the Control offered the preferred level of challenge. The relationships between performance and perception measures revealed that higher levels of engagement, immersion, and presence were associated with better performance in the Control, but this trend did not always hold for Massed Exposure and Highlighting. Furthermore, presence emerged as the primary predictor of performance for select target behavior cues in the Control and Massed Exposure conditions, while immersion and engagement predicted performance of select cues in the Highlighting condition. The findings of the present experiment point to the potential benefit of SBT instructional strategies to improve effectiveness of simulation-based training for behavior cue analysis during RAISR operations. Specifically, the findings suggest that the Massed Exposure strategy has the potential to improve response time when detecting both familiar and novel targets. The results also highlight directions for future research to investigate methods to alter instructional strategy design and delivery in order to improve trainee perceptions of the instruction.

Unmanned Aircraft Systems (UAS) face one common challenge when integrating with the existing manned aircraft population in the National Airspace System (NAS). To unlock the full efficiency of UAS, the UAS integrator must comply with an onboard pilot’s requirement to see-and-avoid other aircraft while operating. Commercially available Detect-and-Avoid (DAA) sensor technologies have been developed to attempt to comply with this requirement. UAS integrators must use these sensors to meet or exceed the performance of a human pilot. This thesis covers research done to integrate an array of commercially made DAA sensors with a large Group 3 UAS both in hardware and software that was later flight tested and evaluated for usability. A fast-time simulation is presented using the principles of the National Aeronautics and Space Administration's (NASA) Detect-and-AvoID Alerting Logic for Unmanned Systems (DAIDALUS). Last, open-source tools are presented to assist future integrators in validating their DAA solutions.

Mini Rotorcraft Unmanned Aerial Vehicles (MRUAVs) flown at low altitude as a part of a human-robot team are potential sources of tactical information for local search missions. Traditionally, their effectiveness in this role has been limited by an inability to intelligently perceive unknown environments or integrate with human team members. Human-robot collaboration provides the theory for building cooperative relationships in this context. This theory, however, only addresses those human-robot teams that are either robot-centered or human-centered in their decision making processes or relationships. This work establishes a new branch of human-robot collaborative theory, Operator Blending, which creates codependent and cooperative relationships between a single robot and human team member for tactical missions. Joint Intension Theory is the basis of this approach, which allows both the human and robot to contribute what each does well in accomplishing the mission objectives. Information processing methods for shared visual information and object tracking take advantage of the human role in the perception process. In addition, coupling of translational commands and the search process establish navigation as the shared basis of communication between the MRUAV and human, for system integration purposes. Observation models relevant to both human and robotic collaborators are tracked through a boundary based approach deemed AIM-SHIFT. A system is developed to classify the semantic and functional relevance of an observation model to local search called the Code of Observational Genetics (COG). These COGs are used to qualitatively map the environment through Qualitative Unsupervised Intelligent Collaborative Keypoint (QUICK) mapping, created to support these methods. / Ph. D.

The rapid, accurate determination of the positions and strengths of sources of dangerous radioactivity takes high priority after a catastrophic event to ensure the safety of personnel, civilians, and emergency responders. This thesis presents approaches and algorithms to autonomously investigate radioactive material using an unmanned aerial vehicle. Performing this autonomous analysis comprises five major steps: ingress from a base of operations to the danger zone, initial detection of radioactive material, measurement of the strength of radioactive emissions, analysis of the data to provide position and intensity estimates, and finally egress from the area of interest back to the launch site. In all five steps, time is of critical importance: faster responses promise potentially saved lives. A time-optimal ingress and egress path planning method solves the first and last steps. Vehicle capabilities and instrument sensitivity inform the development of an efficient search path within the area of interest. Two algorithms—a grid-based recursive Bayesian estimator and a novel radiation contour analysis method—are presented to estimate the position of radioactive sources using simple gross gamma ray event count data from a nondirectional radiation detector. The latter procedure also correctly estimates the number of sources present and their intensities. Ultimately, a complete unsupervised mission is developed, requiring minimal initial operator interaction, that provides accurate characterization of the radiation environment of an area of interest as quickly as reasonably possible. / Master of Science

Research in mobile robotics, unmanned systems, and autonomous man-portable vehicles has grown rapidly over the last decade. This push has taken the problems of robot cognition and behavioral control out of the lab and into the field. Two good examples of this are the DARPA Urban Challenge autonomous vehicle race and the RoboCup robot soccer competition. In these challenges, a mobile robot must be capable of completing complex, sophisticated tasks in a dynamic, partially observable and unpredictable environment. Such conditions necessitate a behavioral programming approach capable of performing high-level action selection in the presence of multiple goals of dynamically changing importance, and noisy, incomplete perception data. In this thesis, an approach to behavioral programming is presented that provides the designer with an intuitive method for building contextual intelligence while preserving the qualities of emergent behavior present in traditional behavior-based programming. This is done by using a modified hierarchical state machine for behavior arbitration in sequence with a command fusion mechanism for cooperative and competitive control. The presented approach is analyzed with respect to portability across platforms, missions, and functional requirements. Specifically, two landmark case-studies, the DARPA Urban Challenge and the International RoboCup Competition are examined. / Master of Science

Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g. road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots' perception data (e.g. images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air. / Ph. D. / Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use <i>a priori</i> information about the scene (<i>e.g.</i> road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots’ perception data (<i>e.g.</i> images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air.