No description available.

No description available.

<p>Unmanned aerial systems (UASs) are at the intersection of robotics and aerospace re-<br> search. Their rise in popularity spurred the growth of interest in urban air mobility (UAM)<br> across the world. UAM promises the next generation of transportation and logistics to be<br> handled by UASs that operate closer to where people live and work. Therefore safety and<br> security of UASs are paramount for UAM operations. Monitoring UAS traffic is especially<br> challenging in urban canyon environments where traditional radar systems used for air traffic<br> control (ATC) are limited by their line of sight (LOS).<br> This thesis explores the design and preliminary results of a target tracking system for<br> urban canyon environments based on a network of camera nodes. A network of stationary<br> camera nodes can be deployed on a large scale to overcome the LOS issue in radar systems<br> as well as cover considerable urban airspace. A camera node consists of a camera sensor, a<br> beacon, a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, and<br> an edge computing device. By leveraging high-precision RTK GNSS receivers and beacons,<br> an automatic calibration process of the proposed system is devised to simplify the time-<br> consuming and tedious calibration of a traditional camera network present in motion capture<br> (MoCap) systems. Through edge computing devices, the tracking system combines machine<br> learning techniques and motion detection as hybrid measurement modes for potential targets.<br> Then particle filters are used to estimate target tracks in real-time within the airspace from<br> measurements obtained by the camera nodes. Simulation in a 40m×40m×15m tracking<br> volume shows an estimation error within 0.5m when tracking multiple targets. Moreover,<br> a scaled down physical test with off-the-shelf camera hardware is able to achieve tracking<br> error within 0.3m on a micro-UAS in real time.</p>

This research investigated the Mission Specialist role in micro unmanned aerial systems (mUAS) and was informed by human-robot interaction (HRI) and technology findings, resulting in the design of an interface that increased the individual performance of 26 untrained CBRN (chemical, biological, radiological, nuclear) responders during two field studies, and yielded formative observations for HRI in mUAS. Findings from the HRI literature suggested a Mission Specialist requires a role-specific interface that shares visual common ground with the Pilot role and allows active control of the unmanned aerial vehicle (UAV) payload camera. Current interaction technology prohibits this as responders view the same interface as the Pilot and give verbal directions for navigation and payload control. A review of interaction principles resulted in a synthesis of five design guidelines and a system architecture that were used to implement a Mission Specialist interface on an Apple iPad. The Shared Roles Model was used to model the mUAS human-robot team using three formal role descriptions synthesized from the literature (Flight Director, Pilot, and Mission Specialist). The Mission Specialist interface was evaluated through two separate field studies involving 26 CBRN experts who did not have mUAS experience. The studies consisted of 52 mission trials to surveil, evaluate, and capture imagery of a chemical train derailment incident staged at Disaster City. Results from the experimental study showed that when a Mission Specialist was able to actively control the UAV payload camera and verbally coordinate with the Pilot, greater role empowerment (confidence, comfort, and perceived best individual and team performance) was reported by a majority of participants for similar tasks; thus, a role-specific interface is preferred and should be used by untrained responders instead of viewing the same interface as the Pilot in mUAS. Formative observations made during this research suggested: i) establishing common ground in mUAS is both verbal and visual, ii) type of coordination (active or passive) preferred by the Mission Specialist is affected by command-level experience and perceived responsibility for the robot, and iii) a separate Pilot role is necessary regardless of preferred coordination type in mUAS. This research is of importance to HRI and CBRN researchers and practitioners, as well as those in the fields of robotics, human-computer interaction, and artificial intelligence, because it found that a human Pilot role is necessary for assistance and understanding, and that there are hidden dependencies in the human-robot team that affect Mission Specialist performance.

Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims to ensure that a nonlinear plant with significant modeling uncertainty behaves like a chosen reference model. MRAC methods attempt to achieve this by representing the modeling uncertainty as a weighted combination of known nonlinear functions, and using a weight update law that ensures weights take on values such that the effect of the uncertainty is mitigated. If the adaptive weights do arrive at an ideal value that best represent the uncertainty, significant performance and robustness gains can be realized. However, most MRAC adaptive laws use only instantaneous data for adaptation and can only guarantee that the weights arrive at these ideal values if and only if the plant states are Persistently Exciting (PE). The condition on PE reference input is restrictive and often infeasible to implement or monitor online. Consequently, parameter convergence cannot be guaranteed in practice for many adaptive control applications. Hence it is often observed that traditional adaptive controllers do not exhibit long-term-learning and global uncertainty parametrization. That is, they exhibit little performance gain even when the system tracks a repeated command. This thesis presents a novel approach to adaptive control that relies on using current and recorded data concurrently for adaptation. The thesis shows that for a concurrent learning adaptive controller, a verifiable condition on the linear independence of the recorded data is sufficient to guarantee that weights arrive at their ideal values even when the system states are not PE. The thesis also shows that the same condition can guarantee exponential tracking error and weight error convergence to zero, thereby allowing the adaptive controller to recover the desired transient response and robustness properties of the chosen reference models and to exhibit long-term-learning. This condition is found to be less restrictive and easier to verify online than the condition on persistently exciting exogenous input required by traditional adaptive laws that use only instantaneous data for adaptation. The concept is explored for several adaptive control architectures, including neuro-adaptive flight control, where a neural network is used as the adaptive element. The performance gains are justified theoretically using Lyapunov based arguments, and demonstrated experimentally through flight-testing on Unmanned Aerial Systems.

Currently there is ongoing research in the field of Mobile Ad-hoc Networks (MANET) for several different scenarios. Research has focused on topology related challenges such as routing mechanisms or addressing systems, as well as security issues like traceability of radio communication or encryption. In addition, there are very specific research interests such as the effects of directional antennas for MANETs or optimized transmission techniques for minimal power consumption or range optimization. Unmanned aerial vehicles (UAVs), and unmanned aerial systems (UAS) in general, need wireless systems in order to communicate. Current UAS are very flexible and allow for a wide spectrum of mission profiles by means of utilizing different UAVs, according to the requirements at hand. Each mission poses special needs and requirements on the internal and external UAS communication and special mission scenarios calling for UAV swarms increase the complexity and require specialized communication solutions. UAS have specific needs not provided by the general research, but are, on the other hand, to diversified to make much use of narrowly focused developments; UAS form a sufficiently large research area for application of MANETs to be considered as an independent group with specialized needs worthy of tailored implementations of MANET principles. MANET research has not tackled a general approach to UAS although some sources show specific applications involving UAVS. This work presents some new aspects for the development of of ad-hoc wireless networks for UAVs and UAS and focuses on their specialties and needs. A general framework for MANET development is proposed. Furthermore, the proposed specific evaluation scenarios provide for a UAS focused comparison of MANET performance.

This paper examines how precision resources, such as Precision Guided Munitions (PGM) and Unmanned Aerial Systems (UAS), can affect the OODA loop decision making cycle. PGMs add precision and force to kinetic strikes, whilst UASs bring precision and endurance to the Intelligence, Surveillance and Reconnaissance (ISR) field. The research is conducted as a qualitative case study with two cases - the first one being Operation Desert Storm (1991) where precision weapons were first introduced in a large scale operation, and the second one being Operation Iraqi Freedom (2003) which was conducted in a similar environment and organization, but with a huge technological advancement when it came to PGMs and UASs. The four phases of the OODA loop are examined separately, and the two cases are compared to reveal any similarities or differences. The results indicate that precision resources have a beneficial impact on the speed and accuracy of all four phases, as well as the overall efficiency of the OODA loop. The results also indicate the importance of having sound intelligence (which cements John Boyd’s claim that Orientation is the most important part of the loop) and that the next challenge after precision and ISR-capabilities might be successful coordination of the joint forces on tactical and operational levels to gain speed even further.

This thesis broadly describes the construction of two kinds of spectroscopic set-ups to analyze properties of various materials. In the first part, construction of a Raman spectrometer and a high-throughput in-vivo detection for early plant abiotic stress responses is described. Following which, the set-up is modified into a microscope employed to study Murine blood components with samples varying in age. Initial Raman set-up is also improvised using a polarizer in order to gain deeper understanding of the vibrational and rotational bonds in water. The second part of the thesis explains the construction of a laser-induced fluorescence (LIF) sensor module. Performance testing and experiments were carried out with the sensor module to monitor stress in vegetation and fruits and also detect toxins found in corn and carcinogenic compounds in gasoline. The module was further mounted to an unmanned aerial vehicle for field surveys and preliminary testing in flight is described.

Storm damages to forested environments pose significant challenges to landowners, land managers, and conservationists alike. Damage scope and scale assessments can be difficult, costly, and time consuming with conventional pedestrian survey techniques. Consumer grade sUAS technology offers an efficient, cost-effective way to accurately assess storm damage in small to moderate sized survey areas (less than 10 km²). Data were collected over a 0.195 km² area of damaged timber within the Kisatchie National Forest in Central Louisiana using a DJI Mavic 2 Pro drone. Collected imagery was processed into an orthomosaic using Agisoft Metashape Professional with a resulting ground sampling distance of 2.58 cm per pixel. Combined X and Y ground distance accuracy r was calculated as 1.39230 meters and a combined horizontal error was calculated as 0.810455526 meters. From the generated orthomosaic, the total storm damage area was estimated as 2.68 Ha, or 6.63 ac based on digitized polygon area calculations.

<p>This dissertation examines the impact of active shooters upon patrons attending large outdoor events. There has been a spike in shooters targeting densely populated spaces in recent years, to include open-air venues. The 2019 Gilroy Garlic Festival was selected for modeling replication using AnyLogic software to test various experiments designed to reduce casualties in the event of an active shooter situation. Through achievement of validation to produce identical outcomes of the real-world Gilroy Garlic Festival shooting, the researcher established a reliable foundational model for experimental purposes. This active shooter research project identifies the need for rapid response efforts to neutralize the shooter(s) as quickly as possible to minimize casualties. Key findings include the importance of armed officers patrolling event grounds to reduce response time, the need for adequate exits during emergency evacuations, incorporation of modern technology to identify the shooter’s location, and applicability of a 1:548 police to patron ratio.</p>