593 research outputs found
Morphing Concept for Multirotor UAVs Enabling Stability Augmentation and Multiple-Parcel Delivery
This paper presents a novel morphing concept for multirotor Unmanned Aerial Vehicles
(UAVs) to optimize the vehicle
ight performance during multi-parcel deliveries. Abrupt
changes in the vehicle weight distribution during a parcel delivery can cause the UAVs to be
unbalanced. This is usually compensated by the vehicle
ight control system but the motors
may need to operate outside their design range which can deteriorate the stability and
performance of the system. Morphing the geometry of a conventional multirotor airframe
enables the vehicle to continuously re-balanced itself which improves the overall vehicle
performance and safety. The paper derives expressions for the static stability of multirotor
UAVs and discusses the experimental implementation of the morphing technology on a Y6
tricopter configuration. Flight test results of multi-parcel delivery scenarios demonstrate
the capability of the proposed technology to balance the throttle outputs of all rotors
Quadrotor team modeling and control for DLO transportation
94 p.Esta Tesis realiza una propuesta de un modelado dinámico para el transporte de sólidos lineales deformables (SLD) mediante un equipo de cuadricópteros. En este modelo intervienen tres factores: - Modelado dinámico del sólido lineal a transportar. - Modelo dinámico del cuadricóptero para que tenga en cuenta la dinámica pasiva y los efectos del SLD. - Estrategia de control para un transporte e ciente y robusto. Diferenciamos dos tareas principales: (a) lograr una con guración cuasiestacionaria de una distribución de carga equivalente a transportar entre todos los robots. (b) Ejecutar el transporte en un plano horizontal de todo el sistema. El transporte se realiza mediante una con guración de seguir al líder en columna, pero los cuadricópteros individualmente tienen que ser su cientemente robustos para afrontar todas las no-linealidades provocadas por la dinámica del SLD y perturbaciones externas, como el viento. Los controladores del cuadricóptero se han diseñado para asegurar la estabilidad del sistema y una rápida convergencia del sistema. Se han comparado y testeado estrategias de control en tiempo real y no-real para comprobar la bondad y capacidad de ajuste a las condiciones dinámicas cambiantes del sistema. También se ha estudiado la escalabilidad del sistema
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
UAS Pilots Code – Annotated Version 1.0
The UAS PILOTS CODE (UASPC) offers recommendations to advance flight safety, ground safety, airmanship, and professionalism.6 It presents a vision of excellence for UAS pilots and operators, and includes general guidance for all types of UAS. The UASPC offers broad guidance—a set of values—to help a pilot interpret and apply standards and regulations, and to confront real world challenges to avoid incidents and accidents. It is designed to help UAS pilots develop standard operating procedures (SOPs), effective risk management,7 safety management systems (SMS), and to encourage UAS pilots to consider themselves aviators and participants in the broader aviation community
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Robotics and Artificial Intelligence (AI) have been inextricably intertwined
since their inception. Today, AI-Robotics systems have become an integral part
of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These
systems are built upon three fundamental architectural elements: perception,
navigation and planning, and control. However, while the integration of
AI-Robotics systems has enhanced the quality our lives, it has also presented a
serious problem - these systems are vulnerable to security attacks. The
physical components, algorithms, and data that make up AI-Robotics systems can
be exploited by malicious actors, potentially leading to dire consequences.
Motivated by the need to address the security concerns in AI-Robotics systems,
this paper presents a comprehensive survey and taxonomy across three
dimensions: attack surfaces, ethical and legal concerns, and Human-Robot
Interaction (HRI) security. Our goal is to provide users, developers and other
stakeholders with a holistic understanding of these areas to enhance the
overall AI-Robotics system security. We begin by surveying potential attack
surfaces and provide mitigating defensive strategies. We then delve into
ethical issues, such as dependency and psychological impact, as well as the
legal concerns regarding accountability for these systems. Besides, emerging
trends such as HRI are discussed, considering privacy, integrity, safety,
trustworthiness, and explainability concerns. Finally, we present our vision
for future research directions in this dynamic and promising field
Quadrotor team modeling and control for DLO transportation
94 p.Esta Tesis realiza una propuesta de un modelado dinámico para el transporte de sólidos lineales deformables (SLD) mediante un equipo de cuadricópteros. En este modelo intervienen tres factores: - Modelado dinámico del sólido lineal a transportar. - Modelo dinámico del cuadricóptero para que tenga en cuenta la dinámica pasiva y los efectos del SLD. - Estrategia de control para un transporte e ciente y robusto. Diferenciamos dos tareas principales: (a) lograr una con guración cuasiestacionaria de una distribución de carga equivalente a transportar entre todos los robots. (b) Ejecutar el transporte en un plano horizontal de todo el sistema. El transporte se realiza mediante una con guración de seguir al líder en columna, pero los cuadricópteros individualmente tienen que ser su cientemente robustos para afrontar todas las no-linealidades provocadas por la dinámica del SLD y perturbaciones externas, como el viento. Los controladores del cuadricóptero se han diseñado para asegurar la estabilidad del sistema y una rápida convergencia del sistema. Se han comparado y testeado estrategias de control en tiempo real y no-real para comprobar la bondad y capacidad de ajuste a las condiciones dinámicas cambiantes del sistema. También se ha estudiado la escalabilidad del sistema
Security for autonomous cyber-physical systems
Remote disablement and control of autonomous cyber-physical systems is possible through the external manipulation of sensory subsystems. Many modern autonomous systems utilize neural networks to fuse and parse data from sensor input streams. We suggest that the application of probabilistic neural network models increases the robustness of machine learning in sensory subsystems. This study compares Probabilistic Backpropagation (PBP) and equivalently sized non-probabilistic models at processing datasets injected with normally distributed noise. Our results suggest that PBP performs with a smaller RMSE and that its estimate of the posterior uncertainty of weights provides insight to the trustworthiness of the model.Lew Wentz FoundationMechanical and Aerospace Engineerin
In-Flight Learning Based Flight Control of an Unmanned Aircraft System
Title from PDF of title page viewed June 3, 2019Dissertation advisor: Travis FieldsVitaIncludes bibliographical references (pages 128-137)Thesis (PH.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2018Unmanned Aerial Vehicles (UAVs) popularity has increased substantially in the last few
years. UAVs capabilities continue to improve as a result of advances in battery technology, communication, navigation systems and electronics. Increased popularity has driven
researchers to improve UAVs reliability and safety which is reflected by the number of
publications and accelerating educational programs interest. UAVs are suited for a wide
range of civilian and military applications; however, UAVs currently can not integrate
with civilian airspace because of stringent safety requirements. Hence, it is necessary to
push the envelope for UAVs design and control so that they can learn from nature and have
more self-aware capabilities to improve safety and reliability. This dissertation addresses
some challenges involved with flight controller learning based on real-time modeling of
UAV.
Plenty of UAV applications require different operational capabilities within a composite mission. These capabilities include landing and taking off using short runways,
while being able to perform missions that require a high cruise speed i.e. tracking applications. A composite mission also requires the aircraft to be able to hover or operate with
low cruise speeds for applications involving stationary moments. All of these different
operational modes require a hybrid aircraft design that combines fixed wing aircraft capabilities and Vertical Take-Off and Landing (VTOL) aircraft capabilities. However, extensive resources required for hybrid aircraft design prohibited the discovery of different
revolutionary designs. The work presented in this dissertation describes the development
of a rapid modeling, prototyping and controller design platform of an unmanned quadrotor aircraft. Three main objectives are investigated: intelligent excitation input design,
real-time parameter estimation, and learning control.
Real-time estimation of dynamic model parameters is important for control adaptation. However, the aircraft model estimation performance can be severely degraded
by an active control system and highly collinear model terms such as those found on a
quadrotor unmanned aircraft. Recursive Fourier Transform Regression was applied to estimate parameters of different model forms/structures and using different excitation levels.
The generated models are utilized to reconfigure a Nonlinear Dynamic Inversion (NDI)
controller considering different testing conditions: normal, failure, and learning flights.
Finally,an intelligent input design technique is proposed which enables autonomous identification of the vehicle’s response modal frequencies and emphasizes excitation power
accordingly.Introduction -- Literature review -- Real-time closed loop system identification of a Quad-copter -- Flight controller learning based on real-time model estimation of a quadrotor aircraft -- Unmanned aircraft system intelligent system identification experiment design -- Conclusion and future work -- Appendix A. Power spectrum of a multisine signal -- Appendix B. Power spectrum of a multisine signa
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