571 research outputs found
Optical Measurement of Airborne Particles on Unmanned Aircraft
Aerosols and clouds are persistent causes of uncertainty in climate and weather models,
which is due to their heterogeneous suspension and occurrence within the atmosphere, and
complex interactions which are chaotic and exist on small scales. Unmanned aerial vehicles
(UAVs) have grown in popularity, and are becoming more commonly used for general atmospheric
measurement, particularly measurement of aerosols and clouds. This thesis presents
and evaluates a synergy between two UAVs, a multi-rotor: the UH-AeroSAM octocopter and
a fixed-wing: the FMI-Talon, and an optical particle instrument: the Universal Cloud and
Aerosol Sounding System. Computational fluid dynamics with Lagrangian particle tracking
(CFD-LPT) was used as a tool for the characterisation of the velocity fields and particle
trajectories around both UAVs. In both instances CFD-LPT was used to develop an operational
envelope, with particular attention to angle of attack constraints and size distribution
perturbation, for the UAV – instrument synergy. UCASS was the first open path instrument
to be used on a UAV, and a good case has been made for its continued use, particularly
on fixed-wing UAVs, which exhibit less complex aerodynamics and superior stability in the
induced sampling airflow through the instrument
Geometric Tracking Control of a Multi-rotor UAV for Partially Known Trajectories
This paper presents a trajectory-tracking controller for multi-rotor unmanned
aerial vehicles (UAVs) in scenarios where only the desired position and heading
are known without the higher-order derivatives. The proposed solution modifies
the state-of-the-art geometric controller, effectively addressing challenges
related to the non-existence of the desired attitude and ensuring positive
total thrust input for all time. We tackle the additional challenge of the
non-availability of the higher derivatives of the trajectory by introducing
novel nonlinear filter structures. We formalize theoretically the effect of
these filter structures on the system error dynamics. Subsequently, through a
rigorous theoretical analysis, we demonstrate that the proposed controller
leads to uniformly ultimately bounded system error dynamics
Design of autonomous robotic system for removal of porcupine crab spines
Among various types of crabs, the porcupine crab is recognized as a highly potential
crab meat resource near the off-shore northwest Atlantic ocean. However, their
long, sharp spines make it difficult to be manually handled. Despite the fact that
automation technology is widely employed in the commercial seafood processing industry,
manual processing methods still dominate in today’s crab processing, which
causes low production rates and high manufacturing costs.
This thesis proposes a novel robot-based porcupine crab spine removal method.
Based on the 2D image and 3D point cloud data captured by the Microsoft Azure
Kinect 3D RGB-D camera, the crab’s 3D point cloud model can be reconstructed
by using the proposed point cloud processing method. After that, the novel point
cloud slicing method and the 2D image and 3D point cloud combination methods are
proposed to generate the robot spine removal trajectory.
The 3D model of the crab with the actual dimension, robot working cell, and endeffector
are well established in Solidworks [1] and imported into the Robot Operating
System (ROS) [2] simulation environment for methodology validation and design optimization.
The simulation results show that both the point cloud slicing method and
the 2D and 3D combination methods can generate a smooth and feasible trajectory.
Moreover, compared with the point cloud slicing method, the 2D and 3D combination
method is more precise and efficient, which has been validated in the real experiment
environment.
The automated experiment platform, featuring a 3D-printed end-effector and crab
model, has been successfully set up. Results from the experiments indicate that the
crab model can be accurately reconstructed, and the central line equations of each
spine were calculated to generate a spine removal trajectory. Upon execution with
a real robot arm, all spines were removed successfully. This thesis demonstrates the
proposed method’s capability to achieve expected results and its potential for application
in various manufacturing processes such as painting, polishing, and deburring
for parts of different shapes and materials
A robotic platform for precision agriculture and applications
Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited.
As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles.
The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances.
Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles.
The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed.
Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork.
The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto.
Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo.
Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive.
I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati.
L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati.
I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo.
Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta
Virtual Structures Based Autonomous Formation Flying Control for Small Satellites
Many space organizations have a growing need to fly several small satellites close together in order to collect and correlate data from different satellite sensors. To do this requires teams of engineers monitoring the satellites orbits and planning maneuvers for the satellites every time the satellite leaves its desired trajectory or formation. This task of maintaining the satellites orbits quickly becomes an arduous and expensive feat for satellite operations centers. This research develops and analyzes algorithms that allow satellites to autonomously control their orbit and formation without human intervention. This goal is accomplished by developing and evaluating a decentralized, optimization-based control that can be used for autonomous formation flight of small satellites. To do this, virtual structures, model predictive control, and switching surfaces are used. An optimized guidance trajectory is also develop to reduce fuel usage of the system. The Hill-Clohessy-Wiltshire equations and the D\u27Amico relative orbital elements are used to describe the relative motion of the satellites. And a performance comparison of the L1, L2, and L∞ norms is completed as part of this work. The virtual structure, MPC based framework combined with the switching surfaces enables a scalable method that allows satellites to maneuver safely within their formation, while also minimizing fuel usage
Get Your Cyber-Physical Tests Done! Data-Driven Vulnerability Assessment of Robotic Vehicle
The rapid growth of robotic aerial vehicles (RAVs) has attracted extensive interest in numerous public and civilian applications, from flying drones to quadrotors. Security of RAV systems has become increasingly challenging as RAV controller software becomes more complex, exposing a growing attack surface. Memory isolation separates the memory space and enforces memory access control via privilege separation to limit the attacker’s capability so that the attacker cannot compromise other software components by exploiting one memory corruption vulnerability. Memory isolation has been adopted into the resource-constrained systems such as RAVs by lightweight privilege mode switching to meet real-time requirements.
In this paper, we propose ARES, a new variable-level vulnerability excavation framework to find deeper bugs from a combined cyber-physical perspective. We present a data-driven method to illustrate that, despite state-of-the-art memory isolation efforts, RAV systems are still vulnerable to adversarial data manipulation attacks. We augment RAV control states with intermediate controller variables by tracing accessible control parameters and vehicle dynamics within the same isolated memory regions. With this expanded state variable space, we apply multivariate statistical analysis to investigate inter-variable quantitative data dependencies and search for vulnerable state variables. ARES utilizes a learning-based method to show how an attacker can exploit memory corruption bugs in a legitimate memory view and elaborately craft adversarial variable values to disrupt a RAV’s safe operations. We demonstrate the feasibility and capability of ARES on the widely-used Ardupilot RAV framework. Our extensive empirical evaluation shows that the attacker may leverage these vulnerable state variables to achieve various RAV failures during its real-time operations, and even evade existing defense solutions
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
Applied Machine Learning for Prediction and Control of Fluid Flows
Modern aerodynamic technologies such as unmanned aerial systems and horizontal axis wind turbines must regularly contend with forces from highly stochastic and turbulent atmospheric gusts. Conventional methods for modeling and controlling fluid flows are limited in their ability to mitigate these aerodynamic forces in real-time. By applying modern machine learning techniques in an experimental setting, this thesis demonstrates the utility of machine learning in addressing these important problems. We follow two complementary approaches towards this goal.
First, we find an end-to-end solution for control in a gusty environment with model-free reinforcement learning. We deploy state-of-the-art reinforcement learning algorithms on a generalized aerodynamic test-bed consisting of an airfoil with motorized trailing edge flaps. The system features embedded flow sensors, enabling the inclusion of flow measurements in state observations. We place this system in a highly irregular wake behind a bluff-body, dynamically mounted on elastic bands and therefore free to oscillate, and train reinforcement learning agents to minimize the net lifting force on the system by controlling the position of the trailing edge flaps. We find that model-free reinforcement learning agents can outperform basic linear controllers in this gusty, turbulent environment. We also show that augmenting state observations with flow measurements can lead to more consistent learning of the system dynamics.
Next, we explore Fourier neural operators (FNOs) as a method for forecasting the time evolution of turbulent fluid flows. FNOs are capable of learning underlying operator solutions to families of partial differential equations and can be evaluated in just milliseconds. We specifically focus on training FNOs with experimentally measured velocity fields of bluff body wakes in the subcritical regime. To the best of our knowledge, this is the first application of operator learning for fluid mechanics that features experimental measurements. We find that FNOs can accurately predict the evolution of these turbulent wakes even when trained with imperfect measurements. We then show that FNOs can quickly adapt to unseen conditions with minimal data and training through transfer learning. Finally, we consider the performance of FNOs over longer prediction horizons. This approach could enable real-time gust prediction capabilities and monitoring for applied aerodynamic systems.</p
MODELING OF INNOVATIVE LIGHTER-THAN-AIR UAV FOR LOGISTICS, SURVEILLANCE AND RESCUE OPERATIONS
An unmanned aerial vehicle (UAV) is an aircraft that can operate without the presence of pilots, either through remote control or automated systems. The first part of the dissertation provides an overview of the various types of UAVs and their design features. The second section delves into specific experiences using UAVs as part of an automated monitoring system to identify potential problems such as pipeline leaks or equipment damage by conducting airborne surveys.Lighter-than-air UAVs, such as airships, can be used for various applications, from aerial photography, including surveying terrain, monitoring an area for security purposes and gathering information about weather patterns to surveillance. The third part reveals the applications of UAVs for assisting in search and rescue operations in disaster situations and transporting natural gas. Using PowerSim software, a model of airship behaviour was created to analyze the sprint-and-drift concept and study methods of increasing the operational time of airships while having a lower environmental impact when compared to a constantly switched-on engine. The analysis provided a reliable percentage of finding the victim during patrolling operations, although it did not account for victim behaviour. The study has also shown that airships may serve as a viable alternative to pipeline transportation for natural gas. The technology has the potential to revolutionize natural gas transportation, optimizing efficiency and reducing environmental impact. Additionally, airships have a unique advantage in accessing remote and otherwise inaccessible areas, providing significant benefits in the energy sector. The employment of this technology was studied to be effective in specific scenarios, and it will be worth continuing to study it for a positive impact on society and the environment
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