Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish realism and relevancy in  unmanned aerial vehicle flight planning. Furthermore, this study incorporates one of the most comprehensive set of criteria identified during our literature search. The simulation results clearly show that the 4D path planning approach is able to provide solutions in complex dynamic environments in which the 3D approach could not find a solution

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

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Hybrid Route Optimisation for Maximum Air to Ground Channel Quality

[EN] The urban air mobility market is expected to grow constantly due to the increased interest in new forms of transportation. Managing aerial vehicles fleets, dependent on rising technologies such as artificial intelligence and automated ground control stations, will require a solid and uninterrupted connection to complete their trajectories. A path planner based on evolutionary algorithms to find the most suitable route has been previously proposed by the authors. Herein, we propose using particle swarm and hybrid optimisation algorithms instead of evolutionary algorithms in this work. The goal of speeding the route planning process and reducing computational costs is achieved using particle swarm and direct search algorithms. This improved path planner efficiently explores the search space and proposes a trajectory according to its predetermined goals: maximum air-to-ground quality, availability, and flight time. 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On-line path planning and robust adaptive path following for underactuated autonomous underwater vehicles

Autonome Unterwasservehikel (AUV) sind für den Einsatz in maritimen Gebieten mit harschen und lebensbedrohlichen Umgebungsbedingungen sowie langen Missionshorizonten unerlässlich. Die Planung solcher Missionen erfolgt dabei oft über einen übergeordneten Missionsplanungsalgorithmus, der auf Grundlage von Umgebungsdaten, wie z.B. Wetter-, Karten- und Sensordaten, Referenzwegpunkte generiert. Aufgrund zeitlich veränderlicher Missionsziele und dynamisch variierender Hindernisse, wie z.B. andere Seefahrzeuge, ist eine flexible Anpassung dieser Referenzwegpunkte zur Laufzeit unvermeidlich. Da in den meisten Anwendungsfällen eine möglichst genaue Durchquerung der Wegpunkte mit vertretbarem Stellaufwand gewünscht ist, fokussiert diese Dissertation auf die Bahnführung von AUV, die durch eine Kombination aus on-line Bahnplanung und nichtlinearen Folgeregelungskonzepten besteht. Wegen der Anforderungen von Folgeregelungen an die Glattheit der Referenzbahn (C2) werden im ersten Teil dieser Arbeit zunächst 3D Bahnplanungsalgorithmen auf Basis von Polynomen 5. Grades vorgestellt, welche die von der Missionsplanung vorgegebenen Wegpunkte interpolieren. Zur Verbesserung der numerischen Eigenschaften sowie der Reduzierung des Rechenaufwands wird dieser Ansatz auf B-Splines übertragen. Durch eine spezielle Pufferung/Fensterung einer bestimmten Anzahl an Wegpunkten wird die zusätzliche Anforderung an die on-line Planung adressiert. Im zweiten Teil der Arbeit werden ausgehend von einer eingehenden mathematischen Modellbildung von AUVs nichtlineare Folgeregelungskonzepte für den vollaktuierten und den unteraktuierten Fall (mehr Freiheitsgrade als Stellgrößen) entwickelt. Für ersteren wird eine Feedback-Linearisierung mit beobachterbasiertem Ansatz und aktiver Störunterdrückung präsentiert. Für den zweiten Fall wird ein robustes, adaptives Regelgesetz zur Kompensation von Modellunsicherheiten und Störungen entworfen. Wegen der Unteraktuierung des Systems, stellt dies eine anspruchsvolle Aufgabe dar, welche basierend auf der direkten Methode von Lyapunov und adaptiver Backstepping-Verfahren gelöst wird. Zur Robustifizierung des adaptiven Reglers kommen Parameter-Projektions-Techniken zum Einsatz. Abschließend werden formale Nachweise der Stabilität der präsentierten Regelungen angeführt und die Leistungsfähigkeit der entwickelten Ansätze anhand von detaillierten Simulationen belegt.Autonomous underwater vehicles (AUVs) are indispensable for use in maritime areas with harsh and life-threatening environmental conditions as well as long mission horizons. The planning of such missions is often carried out via a generic mission planning algorithm. Based on environmental data, e.g. weather, map and sensor data, it generates position reference points or so-called way-points to be followed by the AUV. Due to time-varying mission objectives and dynamically varying obstacles, such as other maritime vehicles, a flexible on-line adaptation of these way-points is unavoidable. In addition, for most applications an accurate crossing of way-points is desirable. Therefore, this dissertation focuses on the path generation and following of AUVs, which consists of a combination of on-line path planning and nonlinear path following concepts. Due to the special requirements for path following controllers on the smoothness of the reference path (C2), in the first part of this thesis, we present a 3D path planning algorithm based on degree 5 polynomials which interpolates the way-points given by the mission planning. In order to improve the numerical properties and to reduce the computational effort, this approach is transferred to B-splines. Using a special buffering/windowing of a certain number of way-points, the additional requirement on the on-line planning is addressed. In the second part of the thesis, mathematical modeling of AUVs is carried out. Based on that, nonlinear path following control concepts for the fully-actuated and the under actuated case (more degrees of freedom than control inputs) are developed. For the former, a feedback linearization controller with an observer-based approach and active disturbance rejection capabilities is presented. For the second case, a robust, adaptive control law is developed for the compensation of modeling uncertainty and disturbances. Owing to the under actuation of the system, the controller design is a challenging task, which is solved based on the direct method of Lyapunov and adaptive backstepping techniques. Moreover, parameter projection is used to robustify the adaptive controller. Finally, formal proofs of the stability of the presented controllers are provided and the performance of the developed approaches is demonstrated by means of detailed simulations