Unmanned Aerial Vehicles (UAVs) are increasingly becoming economical platforms for carrying a variety of sensors. Building flight plans that place sensors properly, temporally and spatially, is difficult. The goal of sensor-driven planning is to automatically generate flight plans based on desired sensor placement and temporal constraints. We propose a simple taxonomy of UAV-enabled sensors, identify a set of generic sensor tasks, and argue that many real-world tasks can be represented by the taxonomy. We present a hierarchical sensor-driven flight planning system capable of generating 2D flights that satisfy desired sensor placement and complex timing and dependency constraints. The system makes use of several well-known planning algorithms and includes a user interface. We conducted a user study to show that sensor-driven planning can be used by non-experts, that it is easier for non-experts than traditional waypoint-based planning, and that it produces better flights than waypoint-based planning. The results of our user study experiment support the claims that sensor-driven planning is usable and that it produces better flights.

A Flight Management System (FMS) performs 4 Dimensional flight planning / Lateral Planning (Calculation of the latitude and longitudes of waypoints), Vertical Planning (Calculation of the altitudes of waypoints) and Temporal Planning(Calculation of Estimated Time of Arrival). Correct and accurate calculation of4D flight path and then guiding the pilot/airplane to track the route in specified accuracy limits in terms of lateral (i.e Required Navigational Performance RNP), vertical (Reduced Vertical Seperation Minima RVSM), and time (Required Time of Arrival RTA) is what FMS performs in brief. Any deviation of planned input values versus actual input values, especially during the emergency cases (i.e burning outoneof engines etc.), causes the aircraft to deviate the plan and requires replanning now taking into consideration the currentsituation. In emergency situations especially in Oceaning Flights (flights whose cruise phase lasts more than 5 hour is called as &ldquo / Oceaning Flights&rdquo / ) Optimum Cruise Phase Flight Route Planning plays a vital role. In avionics domain &ldquo / Optimum&rdquo / does not mean &ldquo / shortest path&rdquo / mainly due to the effect of weather data as wind speed and direction directly affects the groundspeed. In the scope of the current thesis, an algorithm employing dynamic programming paradigms will be designed and implemented to find the optimum flight route planning. A top down approach by making use of aircraft route planning ontology will be implemented to fill the gap between the flight plan specific domain knowledge and optimization techniques employed. Where as the algorithm will be generic by encapsulating the aircraft&rsquo / s performance characteristics / it will be evaluated on C-130 aircraft.

Route specification can be a time consuming process in flight planning, and at the same time, it should be done very diligently for the sake of a safe flight and to reach the target on time. An autonomous decision support system can save time spent on finding the most accurate and shortest route from departure to destination. Different kinds of external sources are used as input for making intelligent decisions by taking advantage of a rule based reasoning approach. In this regard, a rule based decision support concept based on Drools rule engine is proposed in order to ease the workload of pilots during route specification. / Flygruttsplanering är en tidskrävande process vid uppdragsplanering. Den bör göras med stor omsorg för att garantera en säker flygning som når målet på utsatt tid. Ett autonomt beslutstödssystem kan spara tid genom att hitta den säkraste och kortaste rutten mellan avgång och destination. I detta sammanhang används olika externa källor som indata och de ligger till grund för att fatta intelligenta beslut med hjälp av regelbaserat resonerande. Konceptet regelbaserat beslutsstöd är baserat på Drools regelmotor för att minska arbetsbördan för piloter under ruttplanering.

Effective and efficient air transportation systems are crucial to a nation's economy and connectedness. These systems involve capital-intensive facilities and equipment and move millions of people and tonnes of freight every day. As air traffic has continued to increase, the systems necessary to ensure safe and efficient operation will continue to grow more and more complex. Hence, it is imperative that air transport analysts are equipped with the best tools to properly predict and respond to expected air transportation operations. This dissertation aims to improve on those tools currently available to air transportation analysts, while offering new ones. Specifically, this thesis will offer the following: 1) A model for predicting arrival runway occupancy times (AROT); 2) a model for predicting departure runway occupancy times (DROT); and 3) a flight planning model. This thesis will also offer an exploration of the uses of unmanned aerial vehicles for providing wireless communications services. For the predictive models of AROT and DROT, we fit hierarchical Bayesian regression models to the data, grouped by aircraft type using airport physical and aircraft operational parameters as the regressors. Recognizing that many existing air transportation models require distributions of AROT and DROT, Bayesian methods are preferred since their output are distributions that can be directly inputted into air transportation modeling programs. Additionally, we exhibit how analysts will be able to decouple AROT and DROT predictions from the traditional 4 or 5 groupings of aircraft currently in use. Lastly, for the flight planning model, we present a 2-D model using presently available wind data that provides wind-optimal flight routings. We improve over current models by allowing free-flight unconnected to pre-existing airways and by offering finer resolutions over the current 2.5 degree norm. / Ph. D.

Flight planning is a large part of the air traffic operations that are presently being conducted. Airlines strive to achieve the cheapest and most cost effective routes for their flights, resulting in aircraft sometimes flying longer routes in order to avoid expensive airspaces with high unit rates. This issue has been an ongoing obstacle for the Swedish air navigation provider, LFV, as some airlines tend to fly over the Baltic Sea, through the Baltic countries, instead of the shorter route through Swedish airspace. These protracted routes result in extra kilometers being flown yearly,consuming extra fuel, as well as imply a revenue loss to LFV and Sweden. The conclusions of this study is that the airspace dodging behavior generate a revenue loss to LFV, totaling approximately €5 032 354 million per year. Should these flights fly the shortest route between their origin and destination, the before mentioned sum would mean an increase in LFV’s reported revenue from air traffic control services by 2%. Airspace dodging also results in roughly 380 408 superfluous kilometers being flown and 1 874 486 liters of additional fuel being consumed every year.

<p dir="ltr">Methodologies for optimizing UAVs' control for varied environmental conditions have become crucial in the recent development for UAV control sector, yet they are lacking. This research focuses on the dynamism of the Gazebo simulator and PX4 Autopilot flight controller, frequently referenced in academic sectors for their versatility in generating close-to-reality digital environments. This thesis proposed an integrated simulation system that ensures realistic wind and gust interactions in the digital world and efficient data extraction by employing an industrial standard control communication protocol called MAVLink with the also the industry standard ground control software QGroundControl, using real and historical weather information from NOAA database. This study also looks into the potential of reinforcement learning, namely the DDPG algorithm, in determining optimal UAV safety distance, trajectory prediction, and mission planning under wind disruption. The overall goal is to enhance UAV stability and safety in various wind-disturbed conditions. Mainly focusing on minimizing potential collision risks in areas such as streets, valleys, tunnels, or really anywhere has winds and obstacles. The ROS network further enhanced these components, streamlining UAV response analysis in simulated conditions. This research presents a machine-learning approach to UAV flight safety and efficiency in dynamic environments by synthesizing an integrated simulation system with reinforcement learning. And the results model has a high accuracy, reaching 91%, 92%, and 97% accuracy on average in prediction of maximum shifting displacement, and left/right shifting displacement, when testing with real wind parameters from KLAF airport. </p>

Diplomová práce shrnuje nejdůležitější informace o letectví, jako například základní používané termíny, popis letových fází apod. V této práci je popsán flight management system, jeho funkce a schopnosti vytvořit cenově příznivý a současně absolutně spolehlivý letový plán. V další části práce je nastíněna důležitost předpovědi počasí pro bezpečnou a současně cenově příznivou leteckou dopravu. Tato práce je vytvořena v programu Matlab a všechny bloky jsou naprogramovány jako m-funkce. Důležité části kódu jsou z důvodu názornosti zobrazeny jako vývojové diagramy. Praktická část práce je rozdělena do několika podkapitol, kde každá podkapitola popisuje jeden blok z blokového schématu pro výpočet nejistoty odhadované doby příletu. Současně je zde vysvětlena funkce ostatních bloků pro plánování letu, předpověď počasí, kombinování větrů a výpočet odhadnuté doby příletu a její nejistoty.