In this thesis, we consider a swarm of military drones flying over an unfriendly territory, where a drone can be shot down by an enemy with an age-based risk probability. We study the problem of scheduling surveillance image transmissions among the drones with the objective of minimizing the overall casualty. We present Hector, a reinforcement learning-based scheduling algorithm. Specifically, Hector only uses the age of each detected target, a piece of locally available information at each drone, as an input to a neural network to make scheduling decisions. Extensive simulations show that Hector significantly reduces casualties than a baseline round-robin algorithm. Further, Hector can offer comparable performance to a high-performing greedy scheduler, which assumes complete knowledge of global information. / Master of Science / Drones have been successfully deployed by the military. The advancement of machine learning further empowers drones to automatically identify, recognize, and even eliminate adversary targets on the battlefield. However, to minimize unnecessary casualties to civilians, it is important to introduce additional checks and control from the control center before lethal force is authorized. Thus, the communication between drones and the control center becomes critical. In this thesis, we study the problem of communication between a military drone swarm and the control center when drones are flying over unfriendly territory where drones can be shot down by enemies. We present Hector, an algorithm based on machine learning, to minimize the overall casualty of drones by scheduling data transmission. Extensive simulations show that Hector significantly reduces casualties than traditional algorithms.

With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an easily customizable, low-cost drone design with the necessary hardware for autonomous behavior, swarm coordination, and on-board object detection capabilities. Additionally, this thesis outlines the necessary network architecture to handle the interconnection and bandwidth requirements of the drone swarm. The drone on-board system uses a PixHawk 4 flight controller to handle flight mechanics, a Raspberry Pi 4 as a companion computer for general-purpose computing power, and a NVIDIA Jetson Nano Developer Kit to perform object detection in real-time. The implemented network follows the 802.11s standard for multi-hop communications with the HWMP routing protocol. This topology allows drones to forward packets through the network, significantly extending the flight range of the swarm. Our experiments show that the selected hardware and implemented network can provide direct point-to-point communications at a range of up to 1000 feet, with extended range possible through message forwarding. The network also provides sufficient bandwidth for bandwidth intensive data such as live video streams. With an expected flight time of about 17 minutes, the proposed design offers a low-cost drone swarm solution for mid-range aerial surveillance applications.

Den här rapporten beskriver och behandlar resultatet av ett kandidatarbete som utfördes i kursen TDDD96:Kandidatprojekt i programvaruutveckling som ges vid Linköpings universitet. Namnet på projektet som har utförts är Drone Light Display och det beställdes av RISE Research Institutes of Sweden AB. Den division av RISE som har lokaler på Linköpings universitet är inriktade på drönarforskning. Uppdraget blev därför att skapa en algoritm för att detektera och undvika kollision mellan drönare samt implementera ett omkringliggande system för att generera ljusshower med hjälp av drönare som flyger i formation. Projektet utfördes av sju tredjeårsstudenter som studerar till Civilingenjör i data- respektive mjukvaruteknik vid Linköpings universitet. Det resulterade i två användargränssnitt, ett för hantering av ljusshower och ett för statuskontroll av drönare, två servrar och en antikollisionsmodul. Antikollisionsmodulen körs som en separat process på varje drönare och är kompatibel med RISE:s egna styrsystem, Drone Safety Service. De båda servrarna tar emot en rad information från drönarna och sammanställer och skickar sedan ut denna till alla drönare och antikollisionsmodulen. På så sätt får antikollisionsmodulen tillgång till den information som behövs för att kunna ta informerade beslut så att drönarna kan undvika kollisioner. Avslutningsvis innehåller rapporten individuella bidrag från varje projektmedlem inom diverse ämnen som är kopplade till mjukvaruutveckling eller till projektet i allmänhet.

Unmanned Aerial Vehicles (UAV), often referred to as drones, have seen increased use in search and rescue (SAR) missions. Traditionally, these missions involve manual control of each drone for aerial surveillance. As UAV autonomy progresses, the next phase in drone technology consists of a shift to autonomous collaborative multi-drone operations, where drones function collectively in swarms. A significant challenge lies in designing user interfaces that can effectively support UAV pilots in their mission without an overload of information from each drone and of their surroundings. This thesis evaluates important human factors, such as situational awareness (SA) and cognitive workload, within complex search and rescue scenarios, with the goal of increasing trust in multi-drone systems through the design and testing of various components. Conducting these user studies aims to generate insights for the future design of multi-drone systems. Two prototypes were developed with a multi-drone user interface, and simulated a stressful search and rescue mission with high cognitive workload. In the second prototype, a heatmap guided UAV pilots based on the lost person model. The prototypes were tested in a conducted user study with experienced UAV pilots in different SAR organizations across Sweden. The results showed variability in SA while monitoring drone swarms, depending on user interface components and SA levels. The prototypes caused significant cognitive workload, slightly reduced in the heatmap-equipped prototype. Furthermore, there was a marginal increase in trust observed in the prototype with the heatmap. Notably, a lack of manual control raised challenges for the majority of participants and many desired features were suggested by participants. These early expert insights can serve as a starting point for future development of multi-drone systems. / The HERD project, supported by the Innovation Fund Denmark for the DIREC project (9142-00001B)