The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mr

Vision Based Collaborative Localization and Path Planning for Micro Aerial Vehicles

Autonomous micro aerial vehicles (MAV) have gained immense popularity in both the commercial and research worlds over the last few years. Due to their small size and agility, MAVs are considered to have great potential for civil and industrial tasks such as photography, search and rescue, exploration, inspection and surveillance. Autonomy on MAVs usually involves solving the major problems of localization and path planning. While GPS is a popular choice for localization for many MAV platforms today, it suffers from issues such as inaccurate estimation around large structures, and complete unavailability in remote areas/indoor scenarios. From the alternative sensing mechanisms, cameras arise as an attractive choice to be an onboard sensor due to the richness of information captured, along with small size and inexpensiveness. Another consideration that comes into picture for micro aerial vehicles is the fact that these small platforms suffer from inability to fly for long amounts of time or carry heavy payload, scenarios that can be solved by allocating a group, or a swarm of MAVs to perform a task than just one. Collaboration between multiple vehicles allows for better accuracy of estimation, task distribution and mission efficiency. Combining these rationales, this dissertation presents collaborative vision based localization and path planning frameworks. Although these were created as two separate steps, the ideal application would contain both of them as a loosely coupled localization and planning algorithm. A forward-facing monocular camera onboard each MAV is considered as the sole sensor for computing pose estimates. With this minimal setup, this dissertation first investigates methods to perform feature-based localization, with the possibility of fusing two types of localization data: one that is computed onboard each MAV, and the other that comes from relative measurements between the vehicles. Feature based methods were preferred over direct methods for vision because of the relative ease with which tangible data packets can be transferred between vehicles, and because feature data allows for minimal data transfer compared to large images. Inspired by techniques from multiple view geometry and structure from motion, this localization algorithm presents a decentralized full 6-degree of freedom pose estimation method complete with a consistent fusion methodology to obtain robust estimates only at discrete instants, thus not requiring constant communication between vehicles. This method was validated on image data obtained from high fidelity simulations as well as real life MAV tests. These vision based collaborative constraints were also applied to the problem of path planning with a focus on performing uncertainty-aware planning, where the algorithm is responsible for generating not only a valid, collision-free path, but also making sure that this path allows for successful localization throughout. As joint multi-robot planning can be a computationally intractable problem, planning was divided into two steps from a vision-aware perspective. As the first step for improving localization performance is having access to a better map of features, a next-best-multi-view algorithm was developed which can compute the best viewpoints for multiple vehicles that can improve an existing sparse reconstruction. This algorithm contains a cost function containing vision-based heuristics that determines the quality of expected images from any set of viewpoints; which is minimized through an efficient evolutionary strategy known as Covariance Matrix Adaption (CMA-ES) that can handle very high dimensional sample spaces. In the second step, a sampling based planner called Vision-Aware RRT* (VA-RRT*) was developed which includes similar vision heuristics in an information gain based framework in order to drive individual vehicles towards areas that can benefit feature tracking and thus localization. Both steps of the planning framework were tested and validated using results from simulation

Development of a flight control architecture for rotary wing UAVs with model based design approach

This thesis describes the design and implementation of various autopilot software architectures for mini/micro rotary-wing unmanned aerial vehicles by exploiting the modelbased design approach. Nowadays in fact, the tendency for software development is changing from manual coding to automatic code generation, in other words, it is becoming model-based. In general, models can be described as abstractions of systems, they are created to serve particular purposes, for example, to present a user-understandable description of the system or to present information in a more intuitive form. Model-based techniques for software design enables the engineer to reduce drastically development time required for software corrections or modi�cations. Under the various chapters, di�erent flight control techniques are presented with theoretical background and tested via simulations and experimental campaigns. All the navigation and control problems presented below arise in development of embedded software that exploits the innovative model-based design technology. In order to provide validations of the proposed solutions, software for simulation and implementation is specialized for the case of multirotor vehicles, which are becoming very helpful systems for many and varied civil operations. This is the reason why part of the text is devoted to multirotor vehicle dynamics

Survey on Aerial Multirotor Design: a Taxonomy Based on Input Allocation

This paper reviews the impact of multirotor aerial vehicles designs on their abilities in terms of tasks and system properties. We propose a general taxonomy to characterize and describe multirotor aerial vehicles and their design, which we apply exhaustively on the vast literature available. Thanks to the systematic characterization of the designs we exhibit groups of designs having the same abilities in terms of achievable tasks and system properties. In particular, we organize the literature review based on the number of atomic actuation units and we discuss global properties arising from their choice and spatial distribution in the designs. Finally, we provide a discussion on the common traits of the designs found in the literature and the main future open problems

Development of an Indoor Multirotor Testbed for Experimentation on Autonomous Guidance Strategies

Despite the vast popularity of rotary wing unmanned aerial vehicles and research centres that develop their guidance software, there are only a limited number of references that provide an exhaustive description of a step-by-step procedure to build-up a multirotor testbed. In response to such need, the first part of this thesis aims to describe, in detail, the complete procedure to establish and operate an autonomous multirotor unmanned aerial vehicle indoor experimental platform to test and validate guidance, navigation and control strategies. Both hardware and software aspects of the testbed are described to offer a complete understanding of the different aspects. The second part of this thesis focuses on two benchmarks multirotor guidance, navigation and control problems. Initially, the guidance law for an accurate landing manoeuvre is studied. Multirotor usually have a flight time limited to a few minutes. Autonomous landing and docking to a charging station could extend the mission duration of these vehicles. Subsequently, the guidance strategy for the formation flight between two multirotors is considered. In this case, the fundamental goal is an accurate autonomous alignment between two vehicles, each of them behaving as a target and chaser simultaneously. In the last part of this thesis, the problem of minimum energy manoeuvres is tackled. Again, in this case, the motive is to address the limitation in multirotor flight duration. The fundamental objective of this guidance, navigation and control strategy is to determine and implement, in real-time, the minimum energy control histories that transfer the multirotor from its initial point to a given final point. As opposed to conventional guidance strategies, mostly based on proportional-integral-derivative laws, a minimum energy controller allows the vehicle to execute the manoeuvre with a minimum electrical power expenditure

Onboard Robust Nonlinear Control for Multiple Multirotor UAVs

In this thesis, we focus on developing and validating onboard robust nonlinear control approaches for multiple multirotor unmanned aerial vehicles (UAVs), for the promise of achieving nontrivial tasks, such as path following with aggressive maneuvers, navigation in complex environments with obstacles, and formation in group. To fulfill these challenging missions, the first concern comes with the stability of flight control for the aggressive UAV maneuvers with large tilted angles. In addition, robustness of control is highly desired in order to lead the multirotor UAVs to safe and accurate performance under disturbances. Furthermore, efficiency of control algorithm is a crucial element for the onboard implementation with limited computational capability. Finally, the potential to simultaneously control a group of UAVs in a stable fashion is required. All of these concerns motivate our work in this thesis in the following aspects. We first propose the problem of robust control for the nontrivial maneuvers of a multirotor UAV under disturbances. A complete framework is developed to enable the UAV to achieve the challenging tasks, which consists of a nonlinear attitude controller based on the solution of global output regulation problems for the rigid body rotations SO(3), a backstepping-like position controller, a six-dimensional (6D) wrench observer to estimate the unknown force and torque disturbances, and an online trajectory planner based on a model predictive control (MPC) method. We prove the strong convergence properties of the proposed method both in theory and via intensive real-robot experiments of aggressive waypoint navigation and large-tilted path following tasks in the presence of external disturbances, e.g. wind gusts. Secondly, we propose the problem of autonomous navigation of a multirotor UAV in complex scenarios. We present an effective and robust control approach, namely a fast MPC method with the inclusion of nonlinear obstacle avoiding constraints, and implement it onboard the UAV at 50Hz. The developed approach enables the navigation for a multirotor UAV in 3D environments with multiple obstacles, by autonomously deciding to fly over or around the randomly located obstacles. The third problem that is addressed in our work is formation control for a group of multirotor UAVs. We solve this problem by proposing a distributed formation control algorithm for multiple UAVs based on the solution of retraction balancing problem. The algorithm brings the whole group of UAVs simultaneously to a prescribed submanifold that determines the formation shape in an asymptotically stable fashion in 2D and 3D environments. We validate our proposed algorithm via a series of hardware-in-the-loop simulations and real-robot experiments in various formation cases of arbitrary time-varying (e.g. expanding, shrinking or moving) shapes. In the actual experiments, up to 4 multirotors have been implemented to form arbitrary triangular, rectangular and circular shapes drawn by the operator via a human-robot-interaction device. We have also carried out virtual tests using up to 6 onboard computers to achieve a spherical formation and a formation moving through obstacles.In dieser Arbeit konzentrieren wir uns auf die Entwicklung und Validierung von robusten nichtlinearen On-Bord Steuerungsansatzen für mehrere unbemannte Multirotor-Luftfahrzeuge (UAVs), mit dem Ziel, nicht triviale Aufgaben zu erledigen wie z.B. Wegfolge mit aggressiven Manovern, Navigation in komplexen Umgebungen mit Hindernissen und Formationsflug in einer Gruppe. Um diese anspruchsvollen Missionen zu erfullen liegt unser Hauptaugenmerk bei der Stabilität der Flugsteuerung für aggressive UAV Manöver mit steilen Lagewinkeln. Des weiteren ist Kontroll-robustheit sehr wünschenswert, um die Multirotor-UAVs unter Beeinflussung sicher und genau zu steuern. Daruber hinaus ist die Effizienz des Kontrollalgorithmus ein wichtiges Element für die Onboard-Implementierung mit eingeschrankter Rechenfähigkeit. Abschliessend ist das Potenzial, gleichzeitig eine Gruppe von UAVs in stabiler Weise zu kontrollieren, erforderlich. All dies motiviert uns zur Arbeit an den folgenden Aspekten: Zuerst behandeln wir das Problem der robusten Steuerung nichttrivialer Manöver eines Multirotor UAV unter Störeinfluss. Ein komplettes Framework wird entwickelt, welches dem UAV ermöglicht diese anspruchsvollen Aufgaben zu bewältigen. Es beinhaltet einem nichtlinearen Lageregler, basierend auf der Lösung von globalen Ausgangsrege lungsproblemen für Starrkörperrotationen SO(3), einem backstepping basierten Positionsregler, einen sechsdimensionalen (6D) wrench observer um die unbekannten Kraftund Drehmomenteinflusse zu schätzen, sowie einem Online-Trajektorienplaner basierend auf Model Predictive Control (MPC). Wir weisen die starken Konvergenzcharakteristiken der vorgeschlagenen Methode nach, sowohl in der Theorie als auchmittels intensiver Real-roboter-Experimente, mit aggressiver Wegpunktnavigation und Wegfindungsaufgaben in extremer Fluglage in Gegenwart externer Einflüsse, z.B. Windböen. Als nächstes bearbeiten wir das Problem der autonomen Navigation eines Multirotor UAV in komplexen Szenarien. Wir stellen einen effektiven und robusten Steuerungsansatz dar, nämlich eine schnelle MPC-Methode mit der Einbeziehung von nichtlinearer Einschränkungen zur Hindernisvermeidung, und implmenetieren diese an Bord des UAV mit 50Hz. Der entwickelte Ansatz ermöglicht die Navigation eines Multirotor UAVs in 3D-Umgebungen mit mehreren Hindernissen, wobei autonom entschieden wir, über oder um die zufällig gelegenen Hindernisse zu fliegen. Das dritte Problem, das in unserer Arbeit angesprochen wird, ist die Bildungssteuerung für eine Gruppe von Multirotor UAVs. Wir lösen dieses Problem, indem wir einen verteilten Formationskontrollalgorithmus für mehrere UAVs auf der Grundlage der Lösung des Retraction Balancing Problems vorschlagen. Der Algorithmus bringt die ganze Gruppe von UAVs gleichzeitig auf eine vorgeschriebene Untermanigfaltigkeit, welche die Formation in asymtotisch stabiler Weise in 2D- und 3D-Umgebungen bestimmt. Wir validieren unseren vorgeschlagenen Algorithmus uber eine Reihe von Hardware-in-the- ¨ Loop-Simulationen und Real-Roboter-Experimente mit verschiedenen Formationsvarianten in beliebigen zeitveränderlichen (z. B. expandierenden, schrumpfenden oder bewegten) Formen. In den eigentlichen Experimenten wurden bis zu 4 Multirotoren eingesetzt, um beliebige dreieckige, rechteckige und kreisförmige Formen zu bilden, die vom Bediener über eine Mensch-Roboter-Interaktionsvorrichtung vorgezeichnet wurden. Wir haben auch virtuelle Tests mit bis zu 6 Onboard-Computern durchgeführt, um eine sphärische Formation und eine Formation zu erreichen, die sich durch Hindernisse. bewegt

Risk-sensitive motion planning for MAVs based on mission-related fault-tolerant analysis

Multirotor Aerial Vehicles may be fault-tolerant by design when rotor-failure is possible to measure or identify, especially when a large number of rotors are used. For instance, an octocopter can be capable to complete some missions even when a double-rotor fault occurs during the execution. In this paper, we study how a rotor-failure reduces the vehicle control admissible set and its importance with respect to the selected mission, i.e. we perform mission-related fault-tolerant analysis. Furthermore, we propose a risk-sensitive motion-planning algorithm capable to take into account the risks during the planning stage by means of mission-related fault-tolerant analysis. We show that the proposed approach is much less conservative in terms of selected performance measures than a conservative risk planner that assumes that the considered fault will certainly occur during the mission execution. As expected, the proposed risk-sensitive motion planner is also readier for accepting failures during the mission execution than the risk-insensitive approach that assumes no failure will occur

An Omnidirectional Aerial Platform for Multi-Robot Manipulation

The objectives of this work were the modeling, control and prototyping of a new fully-actuated aerial platform. Commonly, the multirotor aerial platforms are under-actuated vehicles, since the total propellers thrust can not be directed in every direction without inferring a vehicle body rotation. The most common fully-actuated aerial platforms have tilted or tilting rotors that amplify the aerodynamic perturbations between the propellers, reducing the efficiency and the provided thrust. In order to overcome this limitation a novel platform, the ODQuad (OmniDirectional Quadrotor), has been proposed, which is composed by three main parts, the platform, the mobile and rotor frames, that are linked by means of two rotational joints, namely the roll and pitch joints. The ODQuad is able to orient the total thrust by moving only the propellers frame by means of the roll and pitch joints. Kinematic and dynamic models of the proposed multirotor have been derived using the Euler- Lagrange approach and a model-based controller has been designed. The latter is based on two control loops: an outer loop for vehicle position control and an inner one for vehicle orientation and roll-pitch joint control. The effectiveness of the controller has been tested by means of numerical simulations in the MATLAB c SimMechanics environment. In particular, tests in free motion and in object transportation tasks have been carried out. In the transportation task simulation, a momentum based observer is used to estimate the wrenches exchanged between the vehicle and the transported object. The ODQuad concept has been tested also in cooperative manipulation tasks. To this aim, a simulation model was considered, in which multiple ODQuads perform the manipulation of a bulky object with unknown inertial parameters which are identified in the first phase of the simulation. In order to reduce the mechanical stresses due to the manipulation and enhance the system robustness to the environment interactions, two admittance filters have been implemented: an external filter on the object motion and an internal one local for each multirotor. Finally, the prototyping process has been illustrated step by step. In particular, three CAD models have been designed. The ODQuad.01 has been used in the simulations and in a preliminary static analysis that investigated the torque values for a rough sizing of the roll-pitch joint actuators. Since in the ODQuad.01 the components specifications and the related manufacturing techniques have not been taken into account, a successive model, the ODQuad.02, has been designed. The ODQuad.02 design can be developed with aluminum or carbon fiber profiles and 3D printed parts, but each component must be custom manufactured. Finally, in order to shorten the prototype development time, the ODQuad.03 has been created, which includes some components of the off-the-shelf quadrotor Holybro X500 into a novel custom-built mechanical frame

Multi-agent Collision Avoidance Using Interval Analysis and Symbolic Modelling with its Application to the Novel Polycopter

Coordination is fundamental component of autonomy when a system is defined by multiple mobile agents. For unmanned aerial systems (UAS), challenges originate from their low-level systems, such as their flight dynamics, which are often complex. The thesis begins by examining these low-level dynamics in an analysis of several well known UAS using a novel symbolic component-based framework. It is shown how this approach is used effectively to define key model and performance properties necessary of UAS trajectory control. This is demonstrated initially under the context of linear quadratic regulation (LQR) and model predictive control (MPC) of a quadcopter. The symbolic framework is later extended in the proposal of a novel UAS platform, referred to as the ``Polycopter" for its morphing nature. This dual-tilt axis system has unique authority over is thrust vector, in addition to an ability to actively augment its stability and aerodynamic characteristics. This presents several opportunities in exploitative control design. With an approach to low-level UAS modelling and control proposed, the focus of the thesis shifts to investigate the challenges associated with local trajectory generation for the purpose of multi-agent collision avoidance. This begins with a novel survey of the state-of-the-art geometric approaches with respect to performance, scalability and tolerance to uncertainty. From this survey, the interval avoidance (IA) method is proposed, to incorporate trajectory uncertainty in the geometric derivation of escape trajectories. The method is shown to be more effective in ensuring safe separation in several of the presented conditions, however performance is shown to deteriorate in denser conflicts. Finally, it is shown how by re-framing the IA problem, three dimensional (3D) collision avoidance is achieved. The novel 3D IA method is shown to out perform the original method in three conflict cases by maintaining separation under the effects of uncertainty and in scenarios with multiple obstacles. The performance, scalability and uncertainty tolerance of each presented method is then examined in a set of scenarios resembling typical coordinated UAS operations in an exhaustive Monte-Carlo analysis