Cooperative control for miniature air vehicles (MAVs) is currently a highly researched topic. There are many application for which MAVs are well suited, including fire monitoring, surveillance and reconaissance, and search and rescue missions. All of these applications can be carried out more effictively by a team of MAVs than by a single vehicle. As technologies for microcontrollers and small sensors have improved so have the capabilities of MAVs. This improvement in MAV performance abilities increases the possibility for cooperative missions. The focus of this research was on cooperative timing missions. The issues faced when dealing with multi-MAV flight include information transfer, real time path planning, and maintenance of a fleet of flight-worthy MAVs. Additional challenges associated with timing missions include path following and velocity control. Two timing scenarios were studied and both of these scenarios were flight tested. The first scenario was a sequenced arrival of the MAVs over a target at a predetermined fly-through heading. The second scenario was a simultaneous arrival of the team ofMAVs over a known target location. The ideas of coordination functions and coordination variables have been employed to achieve coordination. Experimental results verify the feasibility of real time coperative control for a team of MAVs. Initial cooperative timing tests revealed the need for more accurate path following. Accordingly, a new method for path following using vector fields was developed. A vector field of desired ground track headings is calculated and commanded ground track headings are calculated such that ground track heading error and lateral following error decay asymptotically even in the presence of constant wind disturbances. The utilization of ground track heading and ground speed in the path following control, in combination with the vector field methods is what makes this zero-error following possible. Methods for following straight lines and orbits as well as combinations of the lines and circular arcs are presented. The assertions that minimal following errors result when using these methods have been verified experimentally.

In this thesis, we assume observing a pair of trajectories of two objects which could interact with one another and we want to propose a way to test their independence. We formulate basic point process definitions and discuss ways to describe trajectory data. We formulate the theory behind Monte Carlo tests and global envelope testing. In Chapter 2, we propose a parametric model to represent trajectories and derive Maximum Likelihood estimates of its model. We conclude the chapter by exploring the performance of these estimates. In Chapter 3, we propose test statistics used to test for independence using a nonparametric Monte Carlo test based on a random shift approach. We perform a simulation study to assess the performance of these statistics under various conditions and discuss the selection of fine-tuning parameters. Finally, in Chapter 4, we study real data provided by the Voyageurs Wolf Project and apply the proposed tests on real wolf trajectories. 1

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Image-charge detection is emerging as an important tool to analyze heavy and heterogeneous samples because of its unique advantages in measuring highly charged microparticles. Conventional image-charge detection instruments include at least three fundamental components: an ionization source, an aerodynamic particle delivery system, and an image-charge detector. Here I report research efforts that investigated the mechanisms of image-charge detection and proposed some instrumental developments of these components to suit specific research purposes. In Chapter 2, I report an investigation of the electrospray ionization (ESI) mechanism based on an observation that a certain portion of charged particles generated with an ESI source carried charges opposite to the needle which is biased with a high voltage. Both biological and non-biological samples were used to shed a light on the complex process of droplet evolution in ESI. In Chapter 3, I present two novel designs of printed circuit board (PCB) based image-charge detectors. With these detectors, not only the charge and velocity of each microparticle were investigated, but also the two dimensional trajectories, with applications in aerosolized particle beam diagnostics. Chapter 4 shows several designs of the microparticle delivering system aiming to achieve a faster acceleration of sample microparticles. Finally, Chapter 5 presents some thoughts on future directions for these projects.

This thesis deals with the extension of the RoScan scanning system features, making its behaviour more secure and adaptivte during scanning of the object on its whole trajectory. This work contains mathematical model of said manipulator, suggested methods to ensure proper behaviour during singularities. New features were added to the RoScan system such as control panel for manipulator control including new format of trajectory log, moving closer or further away from manipulator’s end effector and non adaptive trajectory testing for singularities. Result of this work is ready-to-use.

The aim of diploma theses is the design of an adaptive robotic workplace. The theoretical part focus on the division of robotic systems and the technical description of individual devices that were used in the implementation of the solution. The practical part contains an analysis of solutions and optimization of the entire production process in terms of minimizing the trajectory, smoothness of movements, time interval, which were analyzed in RoboSim software and in Roboshop software source code was created. Part of the theses is the design for an adaptive production process. The result of the work is an algorithm for controlling robot movements between individual processes. The theses contain a variant solution and possible innovative solutions for possible expansion of the workplace.

Objective of this thesis is about design of grammar and user interface for filtering and visualization of spatiotemporal data. The initial task is to get acquainted with evaluation of traffic data based on trajectory analysis. The next part is the design and description of a formalism which allows spatial filtering and filtering based on static and dynamic attributes. Based on the created formalism, data analysis application with a user interface is designed. Design process was preceded by a comparison of existing solutions. Application is implemented in Qt Framework using C++ and QML languages.

<div>Conceptual aerospace mission design has typically been performed in a computationally intensive and iterative manner. The introduction of modern computing has resulted in the widespread adoption of various numerical methods. As a result, useful information associated with the optimal solution is largely ignored. Optimization through indirect methods, while still computationally intense, leverages this information and also reveals a much deeper mathematical structure. This mathematical structure provides the gateway to reformulating the problem definition to one with certain desirable properties. In the presence of symmetries and constants-of-motion, the dynamical systems of indirect methods live in a reduced dimensional quotient manifold. Studies leveraging this reduced dimensional quotient manifold may benefit in performance by using fewer operations per iteration.</div><div><br></div><div>Many limitations prevent the use of these quotient manifolds in practical aerospace mission design. The five main issues include (1) rephrasing indirect methods in terms of differential geometry in an efficient manner, (2) Pontryagin's minimum principle generating a large number of valid dynamical systems, (3) implementing reduction in a global manner for highly non-linear systems, (4) numerical boundary-value problem solvers not supporting missions on quotient manifolds, and (5) scalability of the methods to real aerospace missions. This work addresses all five issues.</div><div><br></div><div>In previous studies, computer algebra systems have been proven to be an effective tool for automating complex indirect methods. However, when posed in the language of differential geometry, the majority of support from digital software is lost. A version of indirect methods is recasted using differential geometry that effectively retains all information of so-called traditional methods. Exploitation of the anti-symmetric differential structure enables large-scale problems to be studied.</div><div><br></div><div>Root-solving the stationary Hamiltonian condition may generate several, potentially valid dynamical systems. Each system must be evaluated at every point along the trajectory using Pontryagin's minimum principle. This process prohibits later analytical derivations on the dynamical system. In the Integrated Control Regularization Method, the control law is posed as a state of the dynamical system with an equation-of-motion, thereby moving complicated root-solving to the boundary where it is solved once. Introduction of the control law as a new state is done using geometric adjoining methods where the original mathematical structure of the problem is preserved.</div><div><br></div><div>Reduction is traditionally studied in a topological context where there is a wealth of information. In terms of aerospace missions, there are very few applications in existence. These traditional studies rely on the quotient of entire global spaces. This is impossible to apply on non-integrable, non-linear dynamical systems. To get around this, a compact procedure is developed where the Lie algebra identifies dynamical sub-systems that may be effectively eliminated. This removes the reliance of integrability on the symmetry space.</div><div><br></div><div>In reduction, various dimensions desirable to a designer may be eliminated from the system defined on a reduced dimensional quotient manifold. Crucial to satisfying mission requirements, present day numerical solvers do not have the capability to perform the necessary reconstruction. A modified collocation and shooting algorithm with this functionality is given and a numerical example of a problem on a reduced dimensional quotient manifold is explored.</div><div><br></div><div>Including reduction, nearly all advanced analytical techniques on dynamical systems introduce their own set of complexities. Typical aerospace designers do not want to deal with the many difficulties associated with each technique. By formalizing optimal control theory as a composable functorial process, these advanced strategies are compartmentalized with well-defined and predictable results. This enables the use and reuse of many different techniques in series, vastly improving on the automation of indirect methods.</div>

abstract: Control algorithm development for quadrotor is usually based solely on rigid body dynamics neglecting aerodynamics. Recent work has demonstrated that such a model is suited only when operating at or near hover conditions and low-speed flight. When operating in confined spaces or during aggressive maneuvers destabilizing forces and moments are induced due to aerodynamic effects. Studies indicate that blade flapping, induced drag, and propeller drag influence forward flight performance while other effects like vortex ring state, ground effect affect vertical flight performance. In this thesis, an offboard data-driven approach is used to derive models for parasitic (bare-airframe) drag and propeller drag. Moreover, thrust and torque coefficients are identified from static bench tests. Among the two, parasitic drag is compensated for in the position controller module in the PX4 firmware. 2-D circular, straight line, and minimum snap rectangular trajectories with corridor constraints are tested exploiting differential flatness property wherein altitude and yaw angle are constant. Flight tests are conducted at ASU Drone Studio and results of tracking performance with default controller and with drag compensated position controller are presented. Root mean squared tracking error in individual axes is used as a metric to evaluate the model performance. Results indicate that, for circular trajectory, the root mean squared error in the x-axis has reduced by 44.54% and in the y-axis by 39.47%. Compensation in turn degrades the tracking in both axis by a maximum under 12% when compared to the default controller for rectangular trajectory case. The x-axis tracking error for the straight-line case has improved by 44.96% with almost no observable change in the y-axis. / Dissertation/Thesis / Real-time Flight Test of Circular Trajectories / Masters Thesis Aerospace Engineering 2020

<p>The motivation for this research stems from the promise of coupling multi-sensory systems and advanced data analytics to enhance holistic situational awareness and thus prevent fatal accidents in the construction industry. The construction industry is one of the most dangerous industries in the U.S. and worldwide. Occupational Safety and Health Administration (OSHA) reports that the construction sector employs only 5% of the U.S. workforce, but accounts for 21.1% (1,008 deaths) of the total worker fatalities in 2018. The struck-by accident is one of the leading causes and it alone led to 804 fatalities between 2011 and 2015. A critical contributing factor to struck-by accidents is the lack of holistic situational awareness, attributed to the complex and dynamic nature of the construction environment. In the context of construction site safety, situational awareness consists of three progressive levels: perception – to perceive the status of construction entities on the jobsites, comprehension – to understand the ongoing construction activities and interactions among entities, and projection – to predict the future status of entities on the dynamic jobsites. In this dissertation, holistic situational awareness refers to the achievement at all three levels. It is critical because with the absence of holistic situational awareness, construction workers may not be able to correctly recognize the potential hazards and predict the severe consequences, either of which will pose workers in great danger and may result in construction accidents. While existing studies have been successful, at least partially, in improving the perception of real-time states on construction sites such as locations and movements of jobsite entities, they overlook the capability of understanding the jobsite context and predicting entity behavior (i.e., movement) to develop the holistic situational awareness. This presents a missed opportunity to eliminate construction accidents and save hundreds of lives every year. Therefore, there is a critical need for developing holistic situational awareness of the complex and dynamic construction sites by accurately perceiving states of individual entities, understanding the jobsite contexts, and predicting entity movements.<br></p><p>The overarching goal of this research is to minimize the risk of struck-by accidents on construction jobsite by enhancing the holistic situational awareness of the unstructured and dynamic construction environment through a novel data-driven approach. Towards that end, three fundamental knowledge gaps/challenges have been identified and each of them is addressed in a specific objective in this research.<br></p> <p>The first knowledge gap is the lack of methods in fusing heterogeneous data from multimodal sensors to accurately perceive the dynamic states of construction entities. The congested and dynamic nature of construction sites has posed great challenges such as signal interference and line of sight occlusion to a single mode of sensor that is bounded by its own limitation in perceiving the site dynamics. The research hypothesis is that combining data of multimodal sensors that serve as mutual complementation achieves improved accuracy in perceiving dynamic states of construction entities. This research proposes a hybrid framework that leverages vision-based localization and radio-based identification for robust 3D tracking of multiple construction workers. It treats vision-based tracking as the main source to obtain object trajectory and radio-based tracking as a supplementary source for reliable identity information. It was found that fusing visual and radio data increases the overall accuracy from 88% and 87% to 95% and 90% in two experiments respectively for 3D tracking of multiple construction workers, and is more robust with the capability to recover the same entity ID after fragmentation compared to using vision-based approach alone.<br></p> <p>The second knowledge gap is the missing link between entity interaction patterns and diverse activities on the jobsite. With multiple construction workers and equipment co-exist and interact on the jobsite to conduct various activities, it is extremely difficult to automatically recognize ongoing activities only considering the spatial relationship between entities using pre-defined rules, as what has been done in most existing studies. The research hypothesis is that incorporating additional features such as attentional cues better represents entity interactions and advanced deep learning techniques automates the learning of the complex interaction patterns underlying diverse activities. This research proposes a two-step long short-term memory (LSTM) approach to integrate the positional and attentional cues to identify working groups and recognize corresponding group activities. A series of positional and attentional cues are modeled to represent the interactions among entities, and the LSTM network is designed to (1) classify whether two entities belong to the same group, and (2) recognize the activities they are involved in. It was found that by leveraging both positional and attentional cues, the accuracy increases from 85% to 95% compared with cases using positional cues alone. Moreover, dividing the group activity recognition task into a two-step cascading process improves the precision and recall rates of specific activities by about 3%-12% compared to simply conducting a one-step activity recognition.<br></p> <p>The third knowledge gap is the non-determining role of jobsite context on entity movements. Worker behavior on a construction site is goal-based and purposeful, motivated and influenced by the jobsite context including their involved activities and the status of other entities. Construction workers constantly adjust their movements in the unstructured and dynamic workspace, making it challenging to reliably predict worker trajectory only considering their previous movement patterns. The research hypothesis is that combining the movement patterns of the target entity with the jobsite context more accurately predicts the trajectory of the entity. This research proposes a context-augmented LSTM method, which incorporates both individual movement and workplace contextual information, for better trajectory prediction. Contextual information regarding movements of neighboring entities, working group information, and potential destination information is concatenated with movements of the target entity and fed into an LSTM network with an encoder-decoder architecture to predict trajectory over multiple time steps. It was found that integrating contextual information with target movement information can result in a smaller final displacement error compared to that obtained only considering the previous movement, especially when the length of prediction is longer than the length of observation. Insights are also provided on the selection of appropriate methods.<br></p><p>The results and findings of this dissertation will augment the holistic situational awareness of site entities in an automatic way and enable them to have a better understanding of the ongoing jobsite context and a more accurate prediction of future states, which in turn allows the proactive detection of any potential collisions.<br></p>