Moving path following for unmanned aerial vehicles with applications to single and multiple target tracking problems

This paper introduces the moving path following (MPF) problem, in which a vehicle is required to converge to and follow a desired geometric moving path, without a specific temporal specification, thus generalizing the classical path following that only applies to stationary paths. Possible tasks that can be formulated as an MPF problem include tracking terrain/air vehicles and gas clouds monitoring, where the velocity of the target vehicle or cloud specifies the motion of the desired path. We derive an error space for MPF for the general case of time-varying paths in a two-dimensional space and subsequently an application is described for the problem of tracking single and multiple targets on the ground using an unmanned aerial vehicle (UAV) flying at constant altitude. To this end, a Lyapunov-based MPF control law and a path-generation algorithm are proposed together with convergence and performance metric results. Real-world flight tests results that took place in Ota Air Base, Portugal, with the ANTEX-X02 UAV demonstrate the effectiveness of the proposed method.info:eu-repo/semantics/acceptedVersio

Deployment and navigation of aerial drones for sensing and interacting applications

Existing research recognises the critical role played by Unmanned Aerial Vehicles (UAVs) (also referred to as drones) to numerous civilian applications. Typical drone applications include surveillance, wireless communication, agriculture, among many others. One of the biggest challenges is to determine the deployment and navigation of the drones to benefit the most for different applications. Many research questions have been raised about this topic. For example, drone-enabled wildlife monitoring has received much attention in recent years. Unfortunately, this approach results in significant disturbance to different species of wild animals. Moreover, with the capability of rapidly moving communication supply towards demand when required, the drone equipped with a base station, i.e., drone-cell, is becoming a promising solution for providing cellular networks to victims and rescue teams in disaster-affected areas. However, few studies have investigated the optimal deployments of multiple drone-cells with limited backhaul communication distances. In addition, the use of autonomous drones as flying interactors for many real-life applications has not been sufficiently discussed. With superior maneuverability, drone-enabled autonomous aerial interacting can potentially be used on shark attack prevention and animal herding. Nevertheless, previous studies of autonomous drones have not dealt with such applications in much detail. This thesis explores the solutions to all the mentioned research questions, with a particular focus on the deployment and navigation of the drones. First, we provide one of the first investigations into reducing the negative impacts of wildlife monitoring drones by navigation control. Second, we study the optimal placement of a group of drone-cells with limited backhaul communication ranges, aims to maximise the number of served users. Third, we propose a novel method named ‘drone shark shield’, which uses communicating autonomous drones to intervene and prevent shark attacks for protecting swimmers and surfers. Lastly, we introduce one of the first autonomous drone herding systems for mustering a large number of farm animals efficiently. Simulations have been conducted to verify the effectiveness of the proposed approaches. We believe that our findings in this thesis shed new light on the fundamental benefits of autonomous civilian drones

On Optimal Behavior Under Uncertainty in Humans and Robots

Despite significant progress in robotics and automation in the recent decades, there still remains a noticeable gap in performance compared to humans. Although the computation capabilities are growing every year, and are even projected to exceed the capacities of biological systems, the behaviors generated using current computational paradigms are arguably not catching up with the available resources. Why is that? It appears that we are still lacking some fundamental understanding of how living organisms are making decisions, and therefore we are unable to replicate intelligent behavior in artificial systems. Therefore, in this thesis, we attempted to develop a framework for modeling human and robot behavior based on statistical decision theory. Different features of this approach, such as risk-sensitivity, exploration, learning, control, were investigated in a number of publications. First, we considered the problem of learning new skills and developed a framework of entropic regularization of Markov decision processes (MDP). Utilizing a generalized concept of entropy, we were able to realize the trade-off between exploration and exploitation via a choice of a single scalar parameter determining the divergence function. Second, building on the theory of partially observable Markov decision process (POMDP), we proposed and validated a model of human ball catching behavior. Crucially, information seeking behavior was identified as a key feature enabling the modeling of observed human catches. Thus, entropy reduction was seen to play an important role in skillful human behavior. Third, having extracted the modeling principles from human behavior and having developed an information-theoretic framework for reinforcement learning, we studied the real-robot applications of the learning-based controllers in tactile-rich manipulation tasks. We investigated vision-based tactile sensors and the capability of learning algorithms to autonomously extract task-relevant features for manipulation tasks. The specific feature of tactile-based control that perception and action are tightly connected at the point of contact, enabled us to gather insights into the strengths and limitations of the statistical learning approach to real-time robotic manipulation. In conclusion, this thesis presents a series of investigations into the applicability of the statistical decision theory paradigm to modeling the behavior of humans and for synthesizing the behavior of robots. We conclude that a number of important features related to information processing can be represented and utilized in artificial systems for generating more intelligent behaviors. Nevertheless, these are only the first steps and we acknowledge that the road towards artificial general intelligence and skillful robotic applications will require more innovations and potentially transcendence of the probabilistic modeling paradigm

Analyse et détection des trajectoires d'approches atypiques des aéronefs à l'aide de l'analyse de données fonctionnelles et de l'apprentissage automatique

L'amélioration de la sécurité aérienne implique généralement l'identification, la détection et la gestion des événements indésirables qui peuvent conduire à des événements finaux mortels. De précédentes études menées par la DSAC, l'autorité de surveillance française, ont permis d'identifier les approches non-conformes présentant des déviations par rapport aux procédures standards comme des événements indésirables. Cette thèse vise à explorer les techniques de l'analyse de données fonctionnelles et d'apprentissage automatique afin de fournir des algorithmes permettant la détection et l'analyse de trajectoires atypiques en approche à partir de données sol. Quatre axes de recherche sont abordés. Le premier axe vise à développer un algorithme d'analyse post-opérationnel basé sur des techniques d'analyse de données fonctionnelles et d'apprentissage non-supervisé pour la détection de comportements atypiques en approche. Le modèle sera confronté à l'analyse des bureaux de sécurité des vols des compagnies aériennes, et sera appliqué dans le contexte particulier de la période COVID-19 pour illustrer son utilisation potentielle alors que le système global ATM est confronté à une crise. Le deuxième axe de recherche s'intéresse plus particulièrement à la génération et à l'extraction d'informations à partir de données radar à l'aide de nouvelles techniques telles que l'apprentissage automatique. Ces méthodologies permettent d'améliorer la compréhension et l'analyse des trajectoires, par exemple dans le cas de l'estimation des paramètres embarqués à partir des paramètres radar. Le troisième axe, propose de nouvelles techniques de manipulation et de génération de données en utilisant le cadre de l'analyse de données fonctionnelles. Enfin, le quatrième axe se concentre sur l'extension en temps réel de l'algorithme post-opérationnel grâce à l'utilisation de techniques de contrôle optimal, donnant des pistes vers de nouveaux systèmes d'alerte permettant une meilleure conscience de la situation.Improving aviation safety generally involves identifying, detecting and managing undesirable events that can lead to final events with fatalities. Previous studies conducted by the French National Supervisory Authority have led to the identification of non-compliant approaches presenting deviation from standard procedures as undesirable events. This thesis aims to explore functional data analysis and machine learning techniques in order to provide algorithms for the detection and analysis of atypical trajectories in approach from ground side. Four research directions are being investigated. The first axis aims to develop a post-op analysis algorithm based on functional data analysis techniques and unsupervised learning for the detection of atypical behaviours in approach. The model is confronted with the analysis of airline flight safety offices, and is applied in the particular context of the COVID-19 crisis to illustrate its potential use while the global ATM system is facing a standstill. The second axis of research addresses the generation and extraction of information from radar data using new techniques such as Machine Learning. These methodologies allow to \mbox{improve} the understanding and the analysis of trajectories, for example in the case of the estimation of on-board parameters from radar parameters. The third axis proposes novel data manipulation and generation techniques using the functional data analysis framework. Finally, the fourth axis focuses on extending the post-operational algorithm into real time with the use of optimal control techniques, giving directions to new situation awareness alerting systems

Encoding and control of motor prediction and feedback in the cerebellar cortex

University of Minnesota Ph.D. dissertation. August 2017. Major: Neuroscience. Advisor: Timothy Ebner. 1 computer file (PDF); xi, 162 pages.Extensive research implicates the cerebellum as a forward internal model that predicts the sensory consequences of motor commands and compares them to their actual feedback, generating prediction errors that guide motor learning. However, lacking is a characterization of how information relevant to motor control and sensory prediction error is processed by cerebellar neurons. Of major interest is the contribution of Purkinje cells, the primary output neurons of the cerebellar cortex, and their two activity modalities: simple and complex spike discharges. The dominant hypothesis is that complex spikes serve as the sole error signal in the cerebellar cortex. However, no current hypotheses fully explain or are completely consistent with the spectrum of previous experimental observations. To address these major issues, Purkinje cell activity was recorded during a pseudo-random manual tracking task requiring the continuous monitoring and correction for errors. The first hypothesis tested by this thesis was whether climbing fiber discharge controls the information present in the simple spike firing. During tracking, complex spikes trigger robust and rapid changes in the simple spike modulation with limb kinematics and performance errors. Moreover, control of performance error information by climbing fiber discharge is followed by improved tracking performance, suggesting that it is highly important for optimizing behavior. A second hypothesis tested was whether climbing fiber discharge is evoked by errors in movement. Instead, complex spikes are modulated predictively with behavior. Additionally, complex spikes are not evoked as a result of a specific ‘event’ as has been previously suggested. Together, this suggests a novel function of complex spikes, in which climbing fibers continuously optimize the information in the simple spike firing in advance of changes in behavior. A third hypothesis tested is whether the simple spike discharge is responsible for encoding the sensory prediction errors crucial for online motor control. To address this, two novel manipulations of visual feedback during pseudo-random tracking were implemented to assess whether disrupting sensory information pertinent to motor error prediction and feedback modulates simple spike activity. During these manipulations, the simple spike modulation with behavior is consistent with the predictive and feedback components of sensory prediction error. Together, this thesis addresses a major outstanding question in the field of cerebellar physiology and develops a novel hypothesis about the interaction between the two activity modalities of Purkinje cells