This thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrotors. The second contribution of this thesis is to include two separate methods to generate path constraints that capture non-convex position constraints. Using the convexied optimal trajectory generation problem with physical and path constraints, an algorithm is developed that generates fuel optimal trajectories given the initial state and desired final state. As a proof of concept, a quadrotor testbed is developed that utilize a state-of-the-art motion tracking system. The quadrotor is commanded via a ground station where the convexified optimal trajectory generation algorithm is successfully implemented together with a trajectory tracking feedback controller. / text

Considered is a class of cooperative control problems that has a special affine characterization. Included in this class of multi-agent problems are the so called radar deception problem, formation keeping and formation reconfiguration. An intrinsic geometric formulation of the associated constraints unifies this class of problems and it is the first time such a generalization has been presented. Based on this geometric formulation, a real-time motion planning algorithm is proposed to generate dynamically feasible reference trajectories for the class. The proposed approach explicitly considers actuator and operating constraints of the individual agents and constrained dynamics are derived intrinsically for the multi-agent system which makes these constraints transparent. Deriving the constrained dynamics eliminates the need for nonlinear programming to account for the system constraints, making the approach amenable to real-time control. Explicit consideration of actuator and operating limitations and nonholonomic constraints in the design of the reference trajectories addresses the important issue of dynamic feasibility. The motion planning algorithm developed here is verified through simulations for the radar deception, rigid formation keeping and formation reconfiguration problems. A key objective of this study is to advocate a change in paradigm in the approach to formation control by addressing the key issues of dynamic feasibility and computational complexity. The other important contributions of this study are: Unifying formulation of constrained dynamics for a class of problems in formation control through the intrinsic geometry of their nonholonomic and holonomic constraints; Deriving these constrained dynamics in any choice of frame that can even be coordinate free; Explicit consideration of actuator and operating limits in formation control to design dynamically feasible reference trajectories and Developing a real-time, distributed, scalable motion planning algorithm applicable to a class of autonomous multi-agent systems in formation control.

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Nowadays, with the development of electronic and communication technologies, more and more low-cost vehicles such as small, light-weight aircraft are widely applied in all kinds of fields. Ground Station is an essential part of low cost vehicles for the operator to control and monitor the vehicles. In this thesis, Ground Station Display and Flight Management System for Low-Cost Vehicles have been developed.The major objective of this project is to design an intuitive and easy operative Human Machine Interface for displaying and monitoring the flight data and traffic information on ground. Meanwhile, a Graphic User Interface for the Flight Management System has been developed for realizing the waypoints input and flight plan for the vehicles. To fulfill this task, a low-cost hardware and software architecture is presented. Moreover, some COTS tools such as VAPS and MATLAB are applied for the software development because of their Object-Oriented and Rapid Prototype design methods. At the end of project, simulation has been done for the display HMI to test the behaviours of objects and the impacts of display. The trajectory simulation of flight management control panel is also implemented to test the waypoints creation, trajectory generation and smoothing.

Aujourd'hui, l'inspection des ouvrages d'art est réalisée de façon visuelle par des contrôleurs sur l'ensemble de la structure. Cette procédure est couteuse et peut être particulièrement dangereuse pour les intervenants. Pour cela, le développement du système de vision embarquée sur des drones est privilégié ces jours-ci afin de faciliter l'accès aux zones dangereuses.Dans ce contexte, le travail de cette thèse porte sur l'obtention des méthodes originales permettant la planification, la génération des trajectoires de référence, et le suivi de ces trajectoires par une plate-forme aéroportée stationnaire autonome. Ces méthodes devront habiliter une automatisation du vol en présence de perturbations aérologiques ainsi que des obstacles. Dans ce cadre, nous nous sommes intéressés à deux types de véhicules aériens capable de vol stationnaire : le dirigeable et le quadri-rotors.Premièrement, la représentation mathématique du véhicule volant en présence du vent a été réalisée en se basant sur la deuxième loi de Newton. Deuxièmement, la problématique de génération de trajectoire en présence de vent a été étudiée : le problème de temps minimal est formulé, analysé analytiquement et résolu numériquement. Ensuite, une stratégie de planification de trajectoire basée sur les approches de recherche opérationnelle a été développée.Troisièmement, le problème de suivi de trajectoire a été abordé. Une loi de commande non-linéaire robuste basée sur l'analyse de Lyapunov a été proposée. En outre, un pilote automatique basée sur les fonctions de saturations pour un quadri-rotors a été développée.Les méthodes et algorithmes proposés dans cette thèse ont été validés par des simulations. / Today, the inspection of structures is carried out through visual assessments effected by qualified inspectors. This procedure is very expensive and can put the personal in dangerous situations. Consequently, the development of an unmanned aerial vehicle equipped with on-board vision systems is privileged nowadays in order to facilitate the access to unreachable zones.In this context, the main focus in the thesis is developing original methods to deal with planning, reference trajectories generation and tracking issues by a hovering airborne platform. These methods should allow an automation of the flight in the presence of air disturbances and obstacles. Within this framework, we are interested in two kinds of aerial vehicles with hovering capacity: airship and quad-rotors.Firstly, the mathematical representation of an aerial vehicle in the presence of wind has been realized using the second law of newton.Secondly, the question of trajectory generation in the presence of wind has been studied: the problem of minimal time was formulated, analyzed analytically and solved numerically. Then, a strategy of trajectory planning based on operational research approaches has been developed.Thirdly, the problem of trajectory tracking was carried out. A nonlinear robust control law based on Lyapunov analysis has been proposed. In addition, an autopilot based on saturation functions for quad-rotor crafts has been developed.All methods and algorithms proposed in this thesis have been validated through simulations.

The main objective of this thesis is to present a new path planning scheme for solving the shortest (collision-free) path problem for an agent (vehicle) operating in a partially known environment. We present two novel algorithms to solve the planning problem. For both of these approaches we assume that the agent has detailed knowledge of the environment and the obstacles only in the vicinity of its current position. Far away obstacles or the final destination are only partially known and may even change dynamically at each instant of time. The path planning scheme is based on information gathered on-line by the available on-board sensor devices. The solution minimizes the total length of the path with respect to a metric that includes actual path length, along with a risk-induced metric. In order to obtain an approximation of the whole configuration space at different levels of fidelity we use a wavelet approximation scheme. In the first proposed algorithm, the path-planning problem is solved using a multi-resolution cell decomposition of the environment obtained from the wavelet transform. In the second algorithm, we extend the results of the the first one by using the multiresolution representation of the environment in conjunction with a conformal mapping to polar coordinates. By performing the cell decomposition in polar coordinates, we can naturally incorporate sector-like cells that are adapted to the data representation collected by the on-board sensor devices.

Piezoelectric actuators are resonance systems operating principles based on high-frequency oscillations excitation, are used in structures of robots. Observed piezorobots have no additional motion generating structures, but only direct contact points with the static plane. Piezorobot motion trajectory is broken lines, therefore, the classical trajectory formation methods cannot be applied. The main object is motion trajectory formation methods. The main aim of this work is to create motion trajectory formation methods for precision multidimensional piezorobots. The application of these algorithms is piezorobot motion simulation and control software. / Pjezoelektriniai judesio keitikliai – rezonansinės sistemos, kurių veikimo principas pagrįstas aukšto dažnio virpesių žadinimu. Disertacijoje tiriamieji pjezorobotai neturi papildomų judesį generuojančių struktūrų, o tik tiesioginius kontakto su statine plokštuma taškus. Kadangi judesio trajektorija yra laužyta, klasikiniai trajektorijų formavimo metodai netinka. Pagrindinis tyrimo objektas yra judesio trajektorijų formavimo metodai. Pagrindinis disertacijos tikslas – sukurti precizinių daugiamačių pjezorobotų judesio trajektorijų formavimo algoritmus. Sukurtų algoritmų taikymo sritis – pjezorobotų judesio modeliavimo ir valdymo programinė įranga.

Ce travail de thèse s'inscrit dans le développement d'un véhicule autonome en milieu autoroutier. Plus précisément, il s'agit de proposer une architecture unifiée de génération de trajectoires avec une prise de décision prenant en compte les limitations de l'environnement et des informations disponibles actuellement sur un véhicule automatisé.La méthode propose d'une part de générer des trajectoires sous forme de sigmoı̈de dans une représentation spatiotemporelle continue de l'espace de navigation, préalablement réduit par la modélisation d'intervalles sans collision en conditionnominale de conduite. Les paramètres de la sigmoı̈de sont ensuite optimisés par une stratégie de recuit simulé utilisant l'algorithme de prise de décision comme fonction d'évaluation de la trajectoire générée. De cette manière, les problèmes de discrétisation et de découplage position/vitesse sont évités. D'autre part, l'agrégation des théories de logique floue etdes croyances permet une prise de décision sur des critères hétérogènes et des données incertaines. Le formalisme présenté offre la possibilité d'adapter le comportement du véhicule aux passagers, notamment selon leur perception du risque et leur souhait d'une conduite souple ou sportive.L'approche développée a finalement été évaluée et validée en environnement de simulation puis sur un véhicule de test. La brique de planification a alors été intégrée à l'architecture existante du véhicule, en aval des briques de localisation et de perception des obstacles et en amont de la brique de contrôle. / This thesis work is part of the development of a self-driving car in highway environments. More precisely, it aims to propose a unified architecture of trajectory planner and decision-maker taking into account the limitations of the environment and the available data within the current development of sensors technologies (distance limitations, uncertainties).On the one hand, the method generates sigmoid trajectories in a continuous spatiotemporal representation of the evolution space, which is reduced beforehand by modeling collision-free intervals in nominal conditions of driving. The sigmoid parameters are subsequently optimized with a simulated annealing approach that uses the decision-maker algorithm as the evaluation function for the generated trajectory. It thus makes it possible to elude both the discretization and position/speed decoupling problems. On the other hand, the aggregation of fuzzy logic and belief theory allows decision making on heterogeneous criteria and uncertain data. The proposed framework also handles personalization of the vehicle's behavior, depending on the passengers' risk perception and an aggressive or conservative driving style.The presented approach was finally evaluated and validated in a simulation environment, and then in a test vehicle. The planning block was integrated into the existing vehicle's architecture, interfaced with the localization, obstacles' perception and control blocks.

In safety-critical environments, it is crucial to have a backup strategy the system can turn to when facing a potentially unsafe situation. Run-time assurance provides a reliable methodology as a backup strategy. This work introduces a new framework for Run-time assurance, by generating trajectories in real-time using an optimal trajectory generation algorithm, then tracking the trajectory using transverse dynamics to design a feedback control law tailored for each trajectory generated. The generated trajectories are treated as safety backup trajectories that are only executed and followed by the plant if deemed necessary by the Run-time assurance logic. By using the Run-time assurance mechanism the system’s safety is ensured regardless of the behavior of the primary controller for the system with some constraints on the system. The framework assumes full knowledge of the environment and the system dynamics, while treating the trajectory generation part as a black box.

This thesis presents a novel architecture for human-in-the-loop control of multiple drones. The design of such systems must address several challenges at the same time. The drones must avoid collisions with each other and with obstacles in their task environment while following operator's command as closely as possible to navigate their environment. To this end, they should be able to adjust their pre-defined desired formation and, if needed, transition to alternative formations to ensure collision-free operation in their task environment while following the operator's commands. The proposed control strategy is a central algorithm with multiple stages and relies on formulating and solving convex optimization problems in real time to achieve the control objectives. The operator provides reference velocity commands for the flock of drones to move them in the task environment. The algorithm creates linear collision avoidance constraints and distributes the operator's commands among the drones through a number of intermediate steps. It generates reference trajectories for the drones motion by solving a model-based optimization problem over a receding horizon. Conventional trajectory controllers generate the control inputs for individual drones. Prospective formation shapes are obtained for the drones by formulating and solving parallel convex optimizations, considering the operator's reference command and the obstacle-free space. While keeping the convexity of the optimization problem, the proposed algorithm allows for the presence of obstacles in the middle of the formation. This is achieved by properly assigning obstacle-free regions to each agent separately in the formation. In addition, safe convex regions in the form of linear inequality constraints are generated in the direction of the operator's commanded velocity. Moreover, constraints are introduced to avoid inter-drone collisions at each step. Trajectory optimization is formulated as a quadratic programming problem similar to model predictive control schemes to minimize deviation from human operator's command. The effectiveness of the proposed control algorithm is initially verified by simulating two different operational scenarios. Furthermore, the algorithm is implemented on actual hardware to operate a flock of three drones in a laboratory setting. The implementation of the algorithm in C++ utilizes high-performance computation techniques to achieve sufficiently high real-time control update rates for smooth and stable operation of the drones. / Thesis / Master of Applied Science (MASc) / The rise of unmanned aerial vehicle technology and the increase in their accessibility have made them viable solutions for serious missions such as search and rescue operations. Complex cooperative tasks can be conducted via a collection of drones which can show higher levels of robustness and agility as a system. Although repetitive and simple actions can be easily automated, real-world problems are unpredictable in which complex decision-making is involved. Such scenarios can be tackled by the presence of a human supervisor to empower the system with strong cognitive capabilities. This thesis presents a multi-layer control framework for human-in-the-loop operation of a flock of unmanned aerial vehicles. This method continuously optimizes the drones trajectories to adhere as closely as possible to operator's motion commands while avoiding collisions among them and with obstacles in their task environment. This new control framework is successfully validated in both simulations and experiments in a laboratory environment.