Design optimization of small-scale unmanned air vehicles

Ph.DDOCTOR OF PHILOSOPH

A Distributed Intelligent Sensing Approach for Environmental Monitoring Applications

Scientific reports from around the world present us with the undeniable fact that the global ecosystem is undergoing severe change. As this shift accelerates, it is ever more critical that we are able to quantify the local effects of such changes, and further, their implications, from our daily life to the biological processes that put food on our tables. In this thesis, we study the application of sensor network technology to the observation and estimation of highly local phenomena---specifically at a scale between ten to several hundred square meters. Embedding knowledge about the observed process directly into the sensor nodes' behavior via dedicated resource management or control algorithms allows us to deploy dense networks with low power requirements. Ecological systems are notoriously complex. In our work we must thus be highly experimental; it is our highest goal that we construct an approach to environmental monitoring that is not only realistic, but practical for real-world use. Our approach is centered on a commercially available sensor network product, aided by an off-the-shelf quadrotor with minimal customization. We validate our approach through a series of experiments performed from simulation all the way to reality, in deployments lasting days to several months. We motivate the need for local data via two case studies examining physical phenomena. First, employing novel modalities, we study the eclosion of a common agricultural pest. We present our efforts to acquire data that is more local than commonly employed methods, culminating in a six month deployment in a Swiss apple orchard. Next, we apply a environmental fluid dynamics model to enable the estimation of sensible heat flux using an inexpensive sensor. We integrate the sensor with a wireless sensor network and validate its capabilities in a short-term deployment. Acquiring meaningful data on a local scale requires that we advance the state of the art in multiple aspects. Static sensor networks present a classical tension between resolution, autonomy, and accuracy. We explore the performance of algorithms aimed at providing all three, showing explicitly what is required to implement these approaches for real-world applications in an autonomous deployment under uncontrolled conditions. Eventually, spatial resolution is limited by network density. Such limits may be overcome by the use of mobile sensors. We explore the use of an off-the-shelf quadrotor, equipped with environmental sensors, as an additional element in system of heterogeneous sensing nodes. Through a series of indoor and outdoor experiments, we quantify the contribution of a such a mobile sensor, and various strategies for planning its path

Makers at School, Educational Robotics and Innovative Learning Environments

This open access book contains observations, outlines, and analyses of educational robotics methodologies and activities, and developments in the field of educational robotics emerging from the findings presented at FabLearn Italy 2019, the international conference that brought together researchers, teachers, educators and practitioners to discuss the principles of Making and educational robotics in formal, non-formal and informal education. The editors’ analysis of these extended versions of papers presented at FabLearn Italy 2019 highlight the latest findings on learning models based on Making and educational robotics. The authors investigate how innovative educational tools and methodologies can support a novel, more effective and more inclusive learner-centered approach to education. The following key topics are the focus of discussion: Makerspaces and Fab Labs in schools, a maker approach to teaching and learning; laboratory teaching and the maker approach, models, methods and instruments; curricular and non-curricular robotics in formal, non-formal and informal education; social and assistive robotics in education; the effect of innovative spaces and learning environments on the innovation of teaching, good practices and pilot projects

Mobile networks and internet of things infrastructures to characterize smart human mobility

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.This work has been supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by national funds through FCT–Fundação para a Ciência e Tecnologia through project UIDB/04728/202

Makers at School, Educational Robotics and Innovative Learning Environments

This open access book contains observations, outlines, and analyses of educational robotics methodologies and activities, and developments in the field of educational robotics emerging from the findings presented at FabLearn Italy 2019, the international conference that brought together researchers, teachers, educators and practitioners to discuss the principles of Making and educational robotics in formal, non-formal and informal education. The editors’ analysis of these extended versions of papers presented at FabLearn Italy 2019 highlight the latest findings on learning models based on Making and educational robotics. The authors investigate how innovative educational tools and methodologies can support a novel, more effective and more inclusive learner-centered approach to education. The following key topics are the focus of discussion: Makerspaces and Fab Labs in schools, a maker approach to teaching and learning; laboratory teaching and the maker approach, models, methods and instruments; curricular and non-curricular robotics in formal, non-formal and informal education; social and assistive robotics in education; the effect of innovative spaces and learning environments on the innovation of teaching, good practices and pilot projects

The Complete Reference (Volume 4)

This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems. The book is divided into four parts: Part 1 features two papers on navigation, discussing SLAM and path planning. Part 2 focuses on the integration of ROS into quadcopters and their control. Part 3 then discusses two emerging applications for robotics: cloud robotics, and video stabilization. Part 4 presents tools developed for ROS; the first is a practical alternative to the roslaunch system, and the second is related to penetration testing. This book is a valuable resource for ROS users and wanting to learn more about ROS capabilities and features.info:eu-repo/semantics/publishedVersio

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space