Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge

This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is trained. In the latter, the trained DNN, which mimics the conventional controller, controls the system. Unlike the existing papers in the literature, the network is still being trained for different sets of trajectories which are not used in the training phase of DNN. Thanks to the rule-base, which contains the expert knowledge, the proposed framework learns the system dynamics and operational uncertainties in real-time. The experimental results show that the proposed online learning-based approach gives better trajectory tracking performance when compared to the only offline trained network.Comment: corrected version accepted for ICRA 201

Intelligent Control for Fixed-Wing eVTOL Aircraft

Urban Air Mobility (UAM) holds promise for personal air transportation by deploying "flying cars" over cities. As such, fixed-wing electric vertical take-off and landing (eVTOL) aircraft has gained popularity as they can swiftly traverse cluttered areas, while also efficiently covering longer distances. These modes of operation call for an enhanced level of precision, safety, and intelligence for flight control. The hybrid nature of these aircraft poses a unique challenge that stems from complex aerodynamic interactions between wings, rotors, and the environment. Thus accurate estimation of external forces is indispensable for a high performance flight. However, traditional methods that stitch together different control schemes often fall short during hybrid flight modes. On the other hand, learning-based approaches circumvent modeling complexities, but they often lack theoretical guarantees for stability. In the first part of this thesis, we study the theoretical benefits of these fixed-wing eVTOL aircraft, followed by the derivation of a novel unified control framework. It consists of nonlinear position and attitude controllers using forces and moments as inputs; and control allocation modules that determine desired attitudes and thruster signals. Next, we present a composite adaptation scheme for linear-in-parameter (LiP) dynamics models, which provides accurate realtime estimation for wing and rotor forces based on measurements from a three-dimensional airflow sensor. Then, we introduce a design method to optimize multirotor configuration that ensures a property of robustness against rotor failures. In the second part of the thesis, we use deep neural networks (DNN) to learn part of unmodeled dynamics of the flight vehicles. Spectral normalization that regulates the Lipschitz constants of the neural network is applied for better generalization outside the training domain. The resultant network is utilized in a nonlinear feedback controller with a contraction mapping update, solving the nonaffine-in-control issue that arises. Next, we formulate general methods for designing and training DNN-based dynamics, controller, and observer. The general framework can theoretically handle any nonlinear dynamics with prior knowledge of its structure. Finally, we establish a delay compensation technique that transforms nominal controllers for an undelayed system into a sample-based predictive controller with numerical integration. The proposed method handles both first-order and transport delays in actuators and balances between numerical accuracy and computational efficiency to guarantee stability under strict hardware limitations.</p

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

Conception d’un quadrirotor à rotors inclinables pour le suivi de trajectoires agressives

RÉSUMÉ Les quadrirotors sont des plateformes robotiques aériennes peu coûteuses et agiles. Plusieurs applications sont envisageables avec ces robots tels que l’exploration des mines ou les opérations de reconnaissance et sauvetage. Ces missions nécessitent de naviguer dans des environnements encombrés et imprédictibles. Le véhicule utilisé doit pouvoir éviter rapidement des obstacles tout en circulant à haute vitesse. Le quadrirotor étant sous-actionné est limité dans son agressivité puisqu’il doit s’incliner avant d’accélérer. De plus, les contrôleurs conventionnels utilisés ne prédisent pas le comportement qu’aura le véhicule durant la trajectoire en utilisant sa dynamique ce qui l’empêche de planifier assidument les manœuvres complexes. Dans ce contexte, l’objectif principal de ce mémoire est de s’affranchir de ces deux limitations en développant un quadrirotor capable d’incliner ses moteurs pour accélérer plus rapidement et d’utiliser un contrôleur prédictif pour le suivi de trajectoire. Plus spécifiquement, une modification au design conventionnel du quadrirotor est proposée par l’ajout d’un seul actuateur pour permettre des manœuvres agressives dans un seul axe. Puis, un ILQR qui est un contrôleur prédictif sans optimisation numérique, est développé. Celui-ci tient compte de l’état à jour du quadrirotor pour la linéarisation et la résolution du problème de contrôle optimal. En premier lieu, le modèle dynamique du quadrirotor à moteurs inclinables est présenté. Puis, une loi de contrôle basé sur un schéma de contrôle en cascade avec une boucle régulant la dynamique en translation à l’aide d’un ILQR et une autre la dynamique en rotation avec un régulateur PD sont implémentées. Ensuite, la solution proposée est testée en simulation et comparée aux approches conventionnelles tant en termes de conception mécanique qu’en asservissement. L’erreur en suivi de trajectoire est diminuée de plus de 1483% avec un impact supérieur de l’ajout de l’inclinaison des moteurs. Enfin, un prototype expérimental est conçu avec des pièces électroniques et mécaniques standards et largement accessibles. La différence entre le design conventionnel et le quadrirotor à moteurs inclinables est étudiée sur des trajectoires agressives. L’erreur diminue de plus de 26% avec l’ajout d’un actionneur alors qu’en simulation pour la même trajectoire l’erreur diminue de 38% ce qui indique que la même tendance est conservée.----------ABSTRACT Quadrotors are cost-effective and agile aerial robotic platforms. Numerous applications are possible with these robots like mines exploration or search and rescue operations. Nevertheless, these missions require navigating through cluttered and unpredictable environments. The vehicle used for these operations must be able to avoid newly located obstacles fast while travelling at high speeds for time critical missions. Quadrotors are underactuated systems and therefore limited in their overall maneuvers because they need to tilt their whole body before accelerating in a direction. Also, conventional controllers used with these systems don’t predict the behavior of the vehicle during a trajectory by using the systems dynamics which prevents them from planning diligently complex maneuvers. In this context, the main objective of this master thesis is to mitigate these two limitations by developing a quadrotor able to tilt his motors thrust to accelerate faster and to use a predictive controller for the trajectory tracking problem. Specifically, a modification to the conventional quadrotor mechanical system is proposed by adding a single actuator to enable aggressive motions in a single axis. Then, an ILQR, which is a predictive controller and does not require parameter optimization, is developed. The latter is a state- dependent controller who behaves as a nonlinear controller by considering the known updated state of the vehicle to solve the optimal control problem. First, the dynamic model of the quadrotor with tilting motors is found. Then, a control law based on a cascade control scheme with a loop for the translational dynamics regulated by an ILQR controller and another loop for the rotational dynamics with a PD controller is implemented. Afterwards, the proposed solution is tested in simulations and compared with conventional approaches in terms of mechanical design and control. Trajectory tracking error is reduced by more than 1483% with the tilting motors modification having a superior impact on performance. Finally, an experimental prototype is designed with standard electronic and mechanical pieces available off-the-shelf. The difference between the conventional design and the quadrotor with tilting motors is studied on this custom-made quadrotor on aggressive trajectories. The error has decreased by more than 26% by adding an actuator while in simulation for the same trajectory this error decrease by 38% which indicates that the same trend is maintained

The development and evaluation of computer vision algorithms for the control of an autonomous horticultural vehicle

Economic and environmental pressures have led to a demand for reduced chemical use in crop production. In response to this, precision agriculture techniques have been developed that aim to increase the efficiency of farming operations by more targeted application of chemical treatment. The concept of plant scale husbandry (PSH) has emerged as the logical extreme of precision techniques, where crop and weed plants are treated on an individual basis. To investigate the feasibility of PSH, an autonomous horticultural vehicle has been developed at the Silsoe Research Institute. This thesis describes the development of computer vision algorithms for the experimental vehicle which aim to aid navigation in the field and also allow differential treatment of crop and weed. The algorithm, based upon an extended Kalman filter, exploits the semi-structured nature of the field environment in which the vehicle operates, namely the grid pattern formed by the crop planting. By tracking this grid pattern in the images captured by the vehicles camera as it traverses the field, it is possible to extract information to aid vehicle navigation, such as bearing and offset from the grid of plants. The grid structure can also act as a cue for crop/weed discrimination on the basis of plant position on the ground plane. In addition to tracking the grid pattern, the Kalman filter also estimates the mean distances between the rows of lines and plants in the grid, to cater for variations in the planting procedure. Experiments are described which test the localisation accuracy of the algorithms in offline trials with data captured from the vehicle's camera, and on-line in both a simplified testbed environment and the field. It is found that the algorithms allow safe navigation along the rows of crop. Further experiments demonstrate the crop/weed discrimination performance of the algorithm, both off-line and on-line in a crop treatment experiment performed in the field where all of the crop plants are correctly targeted and no weeds are mistakenly treated