PRE2018 3 Group9

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Contents

Preface

Group Members

Name Study Student ID
Claudiu Ion Software Science 1035445
Endi Selmanaj Electrical Engineering 1283642
Martijn Verhoeven Electrical Engineering 1233597
Leo van der Zalm Mechanical Engineering 1232931

Initial Concepts

After discussing various topics we came up with this final list of projects that seemed interesting to us.

  • Drone interception
  • A tunnel digging robot
  • A fire fighting drone for finding people
  • Delivery uav - (blood in Africa, parcels, medicine, etc.)
  • Voice control robot - (general technique that has many applications)
  • A spider robot that can be used to get to hard to reach places

Chosen Project: Drone Interception

Introduction

According to the most recent industry forecast studies, the unmanned aerial systems (UAS) market is expected to reach 4.7 million units by 2020.[1] Nevertheless, regulations and technical challenges need to be addressed before such unmanned aircraft become as common and accepted by the public as their manned counterpart. The impact of an air collision between an UAS and a manned aircraft is a concern to both the public and government officials at all levels. All around the world, the primary goal of enforcing rules for UAS operations into the national airspace is to assure an appropriate level of safety. Therefore, research is needed to determine airborne hazard impact thresholds for collisions between unmanned and manned aircraft or even collisions with people on the ground as this study already shows.[2].

With the recent developments of small and cheap electronics unmanned aerial vehicles (UAVs) are becoming more affordable for the public and we are seeing an increase in the number of drones that are flying in the sky. This has started to pose a number of potential risks which may jeopardize not only our daily lives but also the security of various high values assets such as airports, stadiums or similar protected airspaces. The latest incident involving a drone which invaded the airspace of an airport took place in December 2018 when Gatwick airport had to be closed and hundreds of flights were cancelled following reports of drone sightings close to the runway. The incident caused major disruption and affected about 140000 passengers and over 1000 flights. This was the biggest disruption since ash from an Icelandic volcano shut down all traffic across Europe in 2010.[3]

Tests performed at the University of Dayton Research Institute show the even a small drone can cause major damage to an airliner’s wing if they meet at more than 300 kilometers per hour.[4]

There are other areas in which drones can have a big impact for users, society and enterprise alike. To summarize these are the key domains impacted by the rise of drones:

  • Airport Security
  • Drone Terrorism
  • Privacy Violation
  • Drone Spying (on governments for intelligence gathering)

Problem Statement

The problem statement is: How can unmanned aerial systems (UAS) be used to quickly intercept and stop other unmanned aerial vehicles (UAV) in airborne situations.

A UAV is defined as an unmanned aerial vehicle and differs from a UAS in one major way: a UAV is just referring to the aircraft itself, not the ground control and communications units.[5]

Objectives

  • Determine the best UAS that can intercept another UAV in airborne situations
  • Improve the chosen concept
  • Create a design for the improved concept, including software and hardware
  • Build a prototype
  • Make an evaluation based on the prototype

Project Organisation

Approach

The aim of our project is to deliver a prototype and model on how an interceptive drone can be implemented. The approach to reach this goal contains multiple steps.

Firstly, we will be going to research papers which describe the state of art of such drones and its respective components. This allows our group to get a grasp of the current technology of such a system and introduce us to the new developments in this field. This also helps to create a foundation for the project, which we can develop into. The state of art also gives valuable insight into possible solutions we can think and whether their implementation is feasible given the knowledge we possess and the limited time. The SotA research will be achieved by studying the literature, recent reports from research institutes and the media and analyzing patents which are strongly connected to our project.

Furthermore, we will continue to analyze the problem from a USE – user, society, enterprise – perspective. An important source of this analysis is the state of art research, where the impact of these drone systems in different stakeholders discussed. The USE aspects will be of utmost importance for our project as every engineer should strive to develop new technologies for helping not only the users but also the society as a whole and also avoid the possible consequence of the system they develop. This analysis will finally lead to a list of requirements for our solutions. Moreover, we will discuss the impact of these solutions on the categories listed prior.

Finally, we hope to develop a prototype for an interceptor drone. We do not plan on making a physical prototype as the time of the project is not feasible for this task. We plan on creating a 3D model of the drone and simulating it, showcasing its functionality in real life. To complement this, we also plan on building an Android application, which serves as a dashboard for the drone tracking different parameters about the drones such as position and overall status. To realize this a list of hardware components will be researched, which would be feasible with our project and create a cost-effective product. Concerning the software, a UML diagram will be created first, to represent the system which will be implemented later on. Together with the wiki page, these will be our final deliverables for the project.

Below we summarize the main steps in our approach of the project.

  • Doing research on our chosen project using SotA literature analysis
  • Analyzing the USE aspects and determining the requirements of our system
  • Consider multiple design strategies
  • Choose the Hardware and create the UML diagram
  • Work on the prototype (3D model and mobile application)
  • Evaluate the prototype

Milestones

Within this project there are three major milestones:

  • After week 2, the best UAS is chosen, options for improvements of this system are made and also there is a clear vision on the user. This means that it is known who the users are and what their requirements are.
  • After week 5, the software and hardware are designed for the improved system. Also a prototype has been made.
  • After week 8, the wiki page is finished and updated with the results that were found from testing the prototype. Also future developments are looked into and added to the wiki page.

Deliverables

  • This wiki page, which contains all of our research and findings
  • A presentation, which is a summary of what was done and what our most important results are
  • A prototype

Planning

The plan for the project is given in the form of a table in which every team member has a specific task for each week. There are also group tasks which every team member should work on. The plan also includes a number of milestones and deliverables for the project.

Name Week #1 Week #2 Week #3 Week #4 Week #5 Week #6 Week #7 Week #8
Research Requirements and USE Analysis Hardware Design Software Design Prototype / Concept Proof Reading Future Developments Conclusions
Claudiu Ion Make a draft planning Add requirements for drone on wiki Regulations and Present Situation Write pseudocode for interceptor drone Mobile app development Proof read the wiki page and correct mistakes Review wiki page Make a final presentation
Summarize project ideas Improve introduction Wireframing the Dashboard App Build UML diagram for software architecture Review wiki page
Write wiki introduction Check approach Start design for dashboard mobile app Mobile app development
Find 5 research papers
Endi Selmanaj Research 6 or more papers Elaborate on the SotA Research the hardware components Work on the drone model Work on 3D drone model / prototype Reviewing the Wiki and fixing spelling errors Finalise the Wiki Page Work on final presantation
Write about the USE aspects Review the whole Wiki page Purchase or request the needed hardware Work on the code needed for the electronics Finish the simulation Work on the layout of the Wiki Check on the relaisation of all objectives
Improve approach Draw the schematics of electronics used Start with the simulation Expand on the material on the Wiki more
Check introduction and requirements
Martijn Verhoeven Find 6 or more research papers Update SotA Research hardware options Start on 3D model of drone Work on 3D model Review wiki page Finalise wiki page
Fill in draft planning Check USE Start thinking about electronics layout Make a bill of costs and list of parts Deliver 3D model of drone
Write about objectives
Leo van der Zalm Find 6 or more research papers Update milestones and deliverables Start on 3D model of drone Work on 3D model Review wiki page Continue tasks from week 7 Finish all lose ends
Search information about subject Check SotA Research hardware components Make a bill of costs and list of parts Put in new devellopmets from week 3, 4 and 5 Write future developmetns Write conclusion/results part
Write problem statements USE analysis including references Start looking at the final presentation
Write objectives
Group Work Introduction Expand on the requirements Research hardware components Interface design for the mobile app Start working on the visualisation
Brainstorming ideas Expand on the state of the art Research systems for stopping drones Start working on drone prototype
Find papers (5 per member) Society and enterprise needs Start working on 3d drone model
User needs and user impacts Work on UML activity diagram Start working on the simulation
Define the USE aspects Improve week 1 topics
Milestones Decide on research topic Add requirements to wiki page USE analysis finished Provide a bill of costs and list of parts Mobile app prototype finished Visualisation finished
Add research papers to wiki Add state of the art to wiki page UML activity diagram finished 3D drone model finished
Write introduction for wiki Research into building a drone
Finish planning Research into the costs involved
Deliverables Mobile app prototype Final presentation
3D drone model Wiki page

USE

In this section we will focus more on analysing the different aspects involving users, society and enterprises in the context of interceptor drones. We will start by identifying key stakeholders for each of the categories and proceed by giving a more in depth analysis. After identifying all these stakeholders, we will continue by stating what our project will mainly focus on in terms of stakeholders. Since the topic of interceptor drones is quite vast depending from which angle we choose to tackle the problem, focusing on a specific group of stakeholders will help us produce a better prototype and conduct better research for that group. Moreover, each of these stakeholders experiences different concerns, which are going to be elaborated separately.

Users

When analysing the main users for an interceptor drone, we quickly see that airports are the most interested in having such a technology. This comes as no surprise when we look at the number of incidents involving rogue drones around airports in the last couple of years. Due to the fact that the airspace within and around airports is heavily restricted and regulated, it is clear that unauthorized flying drones are a real danger not only to the operation of airports and airlines, but also to the safety and comfort of passengers. As was the case with previous incidents, intruder drones which are violating airport airspaces lead to airport shutdowns which result in big delay and huge losses.

Another key group of users is represented by governmental agencies and civil infrastructure operators that want to protect certain high value assets such as embassies. As one can imagine, having intruder drones flying above such a place could lead to serious problems such as diplomacy fights or even impact the relations between the two countries involved. Therefore, one could argue that such a drone could indeed be used with malicious intent to directly cause such tense relations. Another good example worth mentioning is the incident involving the match between Serbia and Albania in 2014 when a drone invaded the pitch carrying an Albanian nationalist banner which lead to a pitch invasion by the Serbian fans and full riot. Needless to say, this incident led to retaliations from both Serbians and Albanians which resulted in significant material damage and damaged even more the fragile relations between the two countries [6].

We can also imagine such an interceptor drone being used by the military or other government branches for fending off terrorist attacks. Being able to deploy such a countermeasure (on a battlefield) would improve improve not only the safety of people but would also help in deterring terrorists from carrying our such acts of violence in the first place.

Lastly, a smaller group of users, but still worth taking into account could be represented by individuals who are prone to get targeted by drones, therefore having their privacy violated by such systems. This could be the case with celebrities or other VIPs who are targeted by the media to get more information about their private lives. To summarize, from a user perspective we think that the research which will go into this project can benefit airport security systems the most. One could say that we are taking an utilitarianism approach to solving this problem, as implementing a security systems for airports would produce the greatest good for the greatest number of people.

Society

When thinking how society could benefit from the existence of a system that detects and stops intruder drones, the best example to consider is again the airport scenario. It is already clear that whenever an unauthorized drone enters the restricted airspace of an airport this causes major concern for the safety of the passengers. Moreover, it causes huge delays and creates big problems for the airport’s operations and airlines which will be losing a lot of money. Apart from this, rogue drones around airports cause logistical nightmares for airports and airlines alike since this will not only create bottlenecks in the passengers flow through the airport but airlines might need to divert passengers on other routes and planes. The cargo planes will also suffer delays and this could lead to bigger problems down the supply chain such as medicine not reaching patients in time. All these problems are a great concern for the society as a whole.

Another big issue for society which interceptor drones hope to solve would be the ability to safely stop a rogue drone from attacking large crowds of people at various events for example. For providing the necessary protection in these situations, it is crucial that the interceptor drone acts very quickly and stops the intruder in a safe and controlled manner as fast as possible without putting the lives of other people in danger. Again, when we think in the context of providing the greatest good for the greatest number of people, the airport security example stands out, therefore this is where the main focus of the research of this paper will be aimed at.

Enterprise

When analysing the impact interceptor drones will have on the enterprise in the context of airport security we identify two main players: the airport security and airlines operating from that airport. Moreover, the airlines can be further divided into two categories: those which transport passengers and those that transports cargo (and we can also have airlines that do both).

From the airport’s perspective, a drone sighting near the airport would require a complete shutdown of all operations for at least 30 minutes, as stated by current regulations [7]. As long as the airport is closed, it will lose money and cause huge operational problems if we think at the number of people left stranded all over the airport waiting for the next flight out. Furthermore, when the airport will open again, there will be even more problems caused by congestion since all planes would want to leave at the same time which is obviously not possible. This can actually lead to incidents on the tarmac involving planes, due to improper handling or lack of space in an airport which is potentially already overfilled with planes.

From the airline’s perspective, whether we are talking about passengers transport or cargo, drones violating an airport’s airspace directly translates in huge losses, big delays and unhappy passengers. Not only will the airline need to compensate passengers in case the flight is canceled, but they would also need to support accommodation expenses in some cases. For cargo companies, a delay in delivering packages can literally mean life or dead if we talk about medicine that needs to get to patients. Moreover, disruptions in the transport of goods can greatly impact the supply chain of numerous other businesses and enterprises, thus these types of events (rogue drones near airports) could have even bigger ramifications.

Finally, after analyzing the USE implications of intruder drones in the context of airport security, we will now focus on researching different types of systems than can be deployed in order to not only detect but also stop and catch such intruders as quickly as possible. This will therefore help mitigate both the risks and various negative implications that such events have on the USE stakeholders that were mentioned before.

Requirements

In order to better understand the needs and design for an interceptor drone, a list of requirements is necessary. There are clearly different ways in which a rogue UAV can be detected, intercepted, tracked and stopped. However, the requirements for the interceptor drone need to be analyzed carefully as any design for such a system must ensure the safety of bystandars and minimize all possible risks involved in taking down the rogue UAV. Equally important are the constraints for the interceptor drone and finally the preferences we have for the system. We will now give the RPC table for the autonomous interceptor drone.

ID Requirement Preference Constraint Category
R1 Detect rogue drone LIDAR system for detecting intruders Does NOT require human action Software
R2 Autonomous flight Fully autonomous drone Does NOT require human action Software & Control
R3 Object recognition Accuracy greater than 90% Uses AI bounding box algorithm Software
R4 Detect rogue drone's flying direction Accuracy greater than 70% Software & Hardware
R5 Detect rogue drone's velocity Accuracy greater than 90% Software & Hardware
R6 Track target Tracking targer for at least 10 minutes Allows for operator to correct drone Software & Hardware
R7 Velocity of 40 km/h Drone is as fast as possible Hardware
R8 Flight time of 10 minutes Flight time is maximized Hardware
R9 FPV live feed (with 60 FPS) Drone records and transmits flight video Records all flight video footage Hardware
R10 Camera of 1080p Flight video is as clear as possible Hardware
R11 Stop rogue drone Is always successful Can NOT be violet or endanger others Hardware
R12 Stable connection to operation base Drone is always connected to base If connection is lost drone buffers data Software
R13 Sensor monitoring Drone sends sensor data to base and app All sensor information is sent to base Software
R14 Return to home functionality Drone autonomously returns home Software
R15 Auto take off Control
R16 Auto landing Control
R17 Auto leveling (in flight) Drone is able to fly in heavy weather Does NOT require human action Control
R18 Minimal weight Drone uses carbon fiber materials Hardware
R19 Cargo capacity of 4 kg Drone is able to carry two catching devices Hardware
R20 Portability Drone is portable and easy to transport Does NOT hinder drone's robustness Hardware
R21 Fast deployment Drone can be deployed in under 5 minutes Does NOT hinder drone's robustness Hardware
R22 Minimal costs Drone cost is less than 800 euros Costs

UML Activity Diagram

Activity diagrams, along with use case and state machine diagrams describe what must happen in the system that is being modeled and therefore they are also called behavior diagrams. Since stakeholders have many issues to consider and manage, it is important to communicate what the overall system should do with clarity and map out process flows in a way that is easy to understand. For this, we will give an activity diagram of our system, including the overview of how the interceptor drone will work. By doing this, we hope to demonstrate the logic of our system and also model some of the software architecture elements such as methods, functions and the operation of the drone.

The interception drone

How to catch a drone

The Skywall 100 is a manual drone intercepting device

The next step is to look at the device which we use to intercept the drone. This could be done with another drone, which we suggested above. But there is also another option. This is by shooting the drone down with a specialized gun, like the ‘Skywall 100’ from OpenWorks Engineering.[8] This British company invented a net gun, which is specialized in shooting down drones. It is manual and has a fast reload speed. This way of taken down the drone is easy and fast, but has two big problems. The first one is that the drone drops on the ground after it is shot down. It could fall on people or even worse, conflict enormous damage when the drone is armed with bombs. This is why the ‘Skywall 100’ can not be used in every situation. The other problem is that this gun is manual, and a human life can be at risk in situations when an armed drone has to be taken down.

The interceptor MP 200, made by MALOU-tech

But their remain two alternatives by which another drone is used to catch the violating drone. No human lives will be at risks, and also the violating drone can be delivered at a desired place. The first option is by using an interception drone, which deploys a net in which the done is caught. This is an existing idea. A French company named MALOU-tech, has built the Interceptor MP200.[9] But this way of catching a drone has some side-effects. On the one hand, this interceptor drone can catch a violating drone and deliver it at a desired place. But on the other hand the relative big interceptor drone has to be as fast and viable as the smaller drone, which is not easy to achieve. Also the net is quite rigid and when there is a collision between the net and the violating drone, the interceptor drone has to be stable and able to find balance, otherwise it is going down. Another problem that occurs is that the violating drone is caught in the net, but not sealed in it. It can easily fell out of the net or not even be capable to be caught in the net. Drones with a frame that protect the rotor blades well are not able to being caught because the rotor blades can not get stuck in the net.

A drone catching another one by using a net gun

Drones like the Interceptor MP 200 are good solutions to violating drones which need to be taken down. But we think that there is a better option. When we implement a net gun onto the interceptor drone and remove the big net, this will result in better performances. The drone still has to be able to follow the violating drone and be as fast and viable. But when the shot is aimed correctly, the violating drone is completely stuck in the net and can’t get out. This is important in case that the drone is armed with bombs. In this case we have to be sure that the armed drone is neutralized completely, meaning that we know for sure that I cannot escape anymore or crash. This is an existing idea, and Delft Dynamics built such a drone.[10]

How to build this drone

State of the Art

In this section the State of the Art (or SotA) concerning our project will be discussed.

Tracking/Targeting

To target a moving target from a moving drone, a way of tracking the target is needed. A lot of articles on how to do this, or related to this problem have been published:

  • Moving Target Classification and Tracking from Real-time Video. In this paper describes a way of extracting moving targets from a real-time video stream which can classify them into predefined categories. This is a useful technique which can be mounted to a ground station or to a drone and extract relevant data of the target.[11]
  • Target tracking using television-based bistatic radar. This article describes a way of detecting and tracking airborne targets from a ground based station using radar technology. In order to determine the location and estimate the target’s track, it uses the Doppler shift and bearing of target echoes. This allows for tracking and targeting drones from a large distance. [12]
  • Detecting, tracking, and localizing a moving quadcopter using two external cameras. In this paper a way of tracking and localizing a drone using the bilateral view of two external cameras is presented. This technique can be used to monitor a small airspace and detect and track intruders. [13]
  • Aerial Object Following Using Visual Fuzzy Servoing. In this article a technique is presented to track a 3D moving object from another UAV based on the color information from a video stream with limited info. The presented technique as presented is able to do following and pursuit, flying in formation, as well as indoor navigation. [14]
  • Patent for an interceptor drone tasked to the location of another tracked drone. This patent proposes a system which includes LIDAR detection sensors and dedicated tracking sensors. [15]
  • Patent for detecting, tracking and estimating the speed of vehicles from a moving platform. This patent proposes an algorithm operated by the on-board computing system of an unmanned aerial vehicle that is used to detect and track vehicles moving on a roadway. The algorithm is configured to detect and track the vehicles despite motion created by movement of the UAV.[16]
  • Patent for scanning environments and tracking unmanned aerial vehicles. This patent refers to systems and methods for scanning environments and tracking unmanned aerial vehicles within the scanned environments. It also provides a method for identifying points of interest in an image and generating a map of the region. [17]
  • Algorithms based on Multiplayer Differential Game Theory, such as two-player decomposition approach, maximum principle approach and minimum-time decomposition approach are presented, each arriving to an efficient way of intercepting an attacking UAV but focusing on optimizing a different variable based on the numbers of drones controlled and attacking the UAV. [18]

Autonomous flying

  • Patent for flight control using computer vision. This patent provides methods for computing a three-dimensional relative location of a target with respect to the reference aerial vehicle based on the image of the environment. [19]
  • Cooperative Control Method Algorithm. This paper presents experimental results for the simultaneous interception of targets by a team of UAV’s. It includes an overview of the co-operative control strategy which can also be used in this project’s drones. [20]
  • Framework for Autonomous On-board Navigation. This article presents a framework for independent autonomous flying of a drone solely based on its onboard sensors. In this framework, the high-level navigation, computer vision and control tasks are carried out in an external processing unit. [21]
  • Towards a navigation system for autonomous indoor flying. This article provides a framework for autonomous indoor flying for small UAV’s derived from existing systems for ground based robots. This system can be utilized for indoor drone interception where dodging objects and humans is one of the most important features. [22]
  • Mission path following for an autonomous unmanned airship. In this project (AURORA) multiple flight path following techniques through a set of pre-defined points are described and compared through simulation. The tests were conducted both with and without wind to show the performance of the controllers. [23]
  • Patent for autonomous tracking and surveillance. This patent refers to a method of protecting an asset by imposing a security perimeter around it which is further divided in a number of zones protected by unmanned aerial vehicles.[24]

Stopping drones

  • One way of catching a drone is by shooting at it with a net. Extensive research has been done on shooting nets, mainly for wildlife purposes [25][26]
  • Looking at drones shooting nets specifically, pneumatic launchers have been implemented successfully. [27]
  • Electromagnetic Launchers present another opportunity for launching a net to another drone. This article explains the mathematical model of such a system and shows the power of electromagnetic launchers, which offer very high acceleration speeds.[28]
  • This paper shows another option for shooting a net is using a hybrid pneumatic-electromagnetic launcher, giving the mathematical model of this system and simulation results. It shows that a hybrid system offers a huge control in the acceleration, more so than a simple pneumatic launcher or an electromagnetic one. [29]

General design of drones

  • Design and control of quadrotors with application to autonomous flying. This paper describes a design of a micro quadrotor, its simulation and linear and nonlinear control techniques. The techniques presented in this paper are broadly applicable and can be used in other drone environments, like drone interception activities. [30]
  • Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering. This article improves on previous work on aerodynamic effects impacting quadrotors. These are used to develop new control techniques that allow for more aggressive maneuvering. This is also useful in the pursuit of another agile drone or UAV. [31]

One big challenge surrounding drones is that their flight time is really limited. A way to prevent this and to have drones constantly surveilling the area they are programmed to is with autonomous mid-air battery swapping. [32]

Other

Although the use of drones for intervention is not directly military, this application can be seen as military. In a paper by Bradley Jay Strawser, the duty to employ UAV’s is discussed. It describes why there is nothing wrong in principle with using a UAV’s. [33]

There is also an existing company, in Delft, that is making drone intercepting drones called Delft Dynamics and they have built the DroneCatcher[10]

References

  1. Allianz Global Corporate & Specialty (2016). Rise of the Drones Managing the Unique Risks Associated with Unmanned Aircraft Systems
  2. Federal Aviation Administration (FAA) (2017). UAS Airborne Collision Severity Evaluation Air Traffic Organization, Washington, DC 20591
  3. From Wikipedia, the free encyclopedia (2018). Gatwick Airport drone incident Wikipedia
  4. Pamela Gregg (2018). Risk in the Sky? University of Dayton Research Institute
  5. From Wikipedia, the free encyclopedia (2019). Unmanned aerial vehicle Wikipedia
  6. From Wikipedia, the free encyclopedia (2019). Serbia v Albania (UEFA Euro 2016 qualifying) Wikipedia
  7. Alex Hern, Gwyn Topham (2018). How dangerous are drones to aircraft? The Guardian
  8. OpenWorks Engineering [1]
  9. MALOU-tech [2]
  10. 10.0 10.1 Delft Dynamics DroneCatcher
  11. A.J. Lipton, H.Fujiyoshi, R.S. Patil Moving target classification and tracking from real-time video (1998) Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201)
  12. P.F. Howland Target tracking using television-based bistatic radar (1999) IEE Proceedings - Radar, Sonar and Navigation Volume 146, Issue 3 p. 166 – 174
  13. M. Dreyer, S. Raj, S. Gururajan, J. Glowacki Detecting, Tracking, and Localizing a Moving Quadcopter Using Two External Cameras (2018) 2018 Flight Testing Conference, AIAA AVIATION Forum, (AIAA 2018-4281)
  14. O. Méndez, M. Ángel, M. Bernal, I. Fernando, C. Cervera, P. Alvarez, M. Alvarez, L. Luna, M. Luna, C. Viviana Aerial Object Following Using Visual Fuzzy Servoing (2011)
  15. Brian R. Van Voorst (2017). Intercept drone tasked to location of lidar tracked drone U.S. Patent No. US20170261604A1. Washington, DC: U.S. Patent and Trademark Office
  16. Eric Saund Christopher. Paulson Gregory. Burton Eric Peeters. (2014). System and method for detecting, tracking and estimating the speed of vehicles from a mobile platform U.S. Patent No. US20140336848A1. Washington, DC: U.S. Patent and Trademark Office
  17. Asa Hammond Nathan. Schuett Naimisaranya Das Busek. (2016). Scanning environments and tracking unmanned aerial vehicles U.S. Patent No. US20160292872A1. Washington, DC: U.S. Patent and Trademark Office
  18. Johan M. Reimann USING MULTIPLAYER DIFFERENTIAL GAME THEORY TO DERIVE EFFICIENT PURSUIT-EVASION STRATEGIES FOR UNMANNED AERIAL VEHICLES (2007) School of Electrical and Computer Engineering, Georgia Institute of Technology
  19. Guy Bar-Nahum. Hong-Bin Yoon. Karthik Govindaswamy. Hoang Anh Nguyen. (2018). Flight control using computer vision U.S. Patent No. US20190025858A1. Washington, DC: U.S. Patent and Trademark Office
  20. Timothy W. McLain and Randal W. Beard, Jed M. Kelsey Experimental Demonstration of Multiple Robot Cooperative Target Intercept (2007)
  21. J. J. Lugo, A. Zell Framework for Autonomous On-board Navigation with the AR.Drone (2013)
  22. S. Grzonka, G. Grisetti, W. Burgard Towards a navigation system for autonomous indoor flying(2009)
  23. J.R. Azinheira, E. Carneiro de Paiva, J.G. Ramos, S.S. Beuno Mission path following for an autonomous unmanned airship (2000)
  24. Kristen L. Kokkeby Robert P. Lutter Michael L. Munoz Frederick W. Cathey David J. Hilliard Trevor L. Olson (2008). System and methods for autonomous tracking and surveillance U.S. Patent No. US20100042269A1. Washington, DC: U.S. Patent and Trademark Office
  25. STEPHEN L. WEBB, JOHN S. LEWIS, DAVID G. HEWITT, MICKEY W. HELLICKSON, FRED C. BRYANT Assessing the Helicopter and Net Gun as a Capture Technique for White‐Tailed Deer (2008) The Journal of Wildlife Management Volume 72, Issue 1,
  26. Andrey Evgenievich Nazdratenko (2007) Net throwing device U.S. Patent No. US20100132580A1. Washington, DC: U.S. Patent and Trademark Office
  27. Mohammad Rastgaar Aagaah, Evandro M. Ficanha, Nina Mahmoudian (2016) Drone with pneumatic net launcher U.S. Patent No. US20170144756A1. Washington, DC: U.S. Patent and Trademark Office
  28. Leubner, Karel & Laga, Radim & Dolezel, Ivo. (2015) Advanced Model of Electromagnetic Launcher Advanced Model of Electromagnetic Launcher. Advances in Electrical and Electronic Engineering. 13. 223-229. 10.15598/aeee.v13i3.1419
  29. Domin, Jaroslaw & Kluszczyński, K. (2013) Hybrid pneumatic-electromagnetic launcher Hybrid pneumatic-electromagnetic launcher - general concept, mathematical model and results of simulation. 89. 21-25.
  30. S. Bouabdallah, R. Siegwart Design and control of quadrotors with application to autonomous flying (2007)
  31. H. Huang, G. M. Hoffmann, S. L. Waslander, C. J. Tomlin Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering (2009)
  32. Jacobsen, Reed; Ruhe, Nikolai; and Dornback, Nathan Autonomous UAV Battery Swapping (2018)
  33. Bradley Jay Strawser Moral Predators: The Duty to Employ Uninhabited Aerial Vehicles Journal of Military Ethics, Volume 9, 2010 – Issue 4
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