PRE2018 3 Group9: Difference between revisions
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Revision as of 18:25, 9 February 2019
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]
Problem Statement
The problem statement is: How can UAS be used to quickly intercept and stop other UAVs 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
- Doing research on our chosen project using SotA literature analysis
- Determine users and requirements
- Consider multiple design strategies
- Work on design (soft and hardware)
- Work on prototype (soft and hardware)
- Evaluate 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
Users
Requirements
State of the Art (SotA)
In this section the State of the Art (or SotA) concerning our project will be discussed.
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 [6][7]
In order to aim at a moving target from a moving drone, a way of tracking the target is needed. Multiple ways of doing this have been researched:
If we look at intercepting another drone, this is done autonomously. References to different articles on autonomous flying:
- On board navigation[11]
- Design and control of quadrotors [12]
- Autonomous indoor flying [13]
- Agressive maneuvering [14]
- Path-following [15]
- Aerial object following [16]
Research Articles, Patents and Relevant Sources
Apart from the various research papers on the topic of interceptor drones, there are also several patents and relevant articles for such systems.
- Patent for an interceptor drone tasked to the location of another tracked drone. This patent proposes a system which includes a LIDAR camera which provides detection and tracking of an intruder drone.[17]
- Patent for autonomous tracking and surveilance. This patent refers to a method of protecting an asset by imposing a security perimiter around it which is further divided in a number of zones protected by unmanned aerial vehicles.[18]
- 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.[19]
- 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.[20]
- 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.[21]
There is also an existing company, in Delft, that is making drone intercepting drones called Delft Dynamics and they have built the DroneCatcher[22]
References
- ↑ Allianz Global Corporate & Specialty (2016). Rise of the Drones Managing the Unique Risks Associated with Unmanned Aircraft Systems
- ↑ Federal Aviation Administration (FAA) (2017). UAS Airborne Collision Severity Evaluation Air Traffic Organization, Washington, DC 20591
- ↑ From Wikipedia, the free encyclopedia (2018). Gatwick Airport drone incident Wikipedia
- ↑ Pamela Gregg (2018). Risk in the Sky? University of Dayton Research Institute
- ↑ From Wikipedia, the free encyclopedia (2019). Unmanned aerial vehicle Wikipedia
- ↑ 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,
- ↑ Andrey Evgenievich Nazdratenko (2007) Net throwing device U.S. Patent No. US20100132580A1. Washington, DC: U.S. Patent and Trademark Office
- ↑ 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)
- ↑ P.F. Howland Target tracking using television-based bistatic radar (1999) IEE Proceedings - Radar, Sonar and Navigation Volume 146, Issue 3 p. 166 – 174
- ↑ 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)
- ↑ J. J. Lugo, A. Zell Framework for Autonomous On-board Navigation with the AR.Drone (2013)
- ↑ S. Bouabdallah, R. Siegwart Design and control of quadrotors with application to autonomous flying (2007)
- ↑ S. Grzonka, G. Grisetti, W. Burgard Towards a navigation system for autonomous indoor flying(2009)
- ↑ H. Huang, G. M. Hoffmann, S. L. Waslander, C. J. Tomlin Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering (2009)
- ↑ J.R. Azinheira, E. Carneiro de Paiva, J.G. Ramos, S.S. Beuno Mission path following for an autonomous unmanned airship (2000)
- ↑ 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)
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ Delft Dynamics [1]