PRE2017 3 Group 17 - State of the Art: Difference between revisions

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We found an article describing two “cleaning” algorithms for swarms of robots. It explains how these algorithms can be used as “hunting” protocols to, for instance, find lost humans. The first of the two algorithms, Parallel Path Search, works on rectangular areas only and requires them to have no “leakage”, but is computationally quite light. Leakage is a term used to describe the phenomenon of dirt leaking from dirty sectors to clean sectors or, closer to the example, the opportunity for a fugitive to escape when gaps between drones are too large. The second, the SWEEP protocol, on the other hand, will work on any non-disconnected area and can work around "leakage", but is computationally quite expensive at times and requires an overview of the entire area. The DDDAS framework uses the best of Parallel Path Search and the SWEEP protocol to create a better framework, that also takes potential loss of drones into account. All in all, some very interesting algorithms already exist for the theme in mind. <ref name="SwarmControl">[https://ac-els-cdn-com.dianus.libr.tue.nl/S1877050913005796/1-s2.0-S1877050913005796-main.pdf?_tid=97585fa4-0be9-11e8-a6b1-00000aacb35f&acdnat=1517996033_0b095911bc2b3916dd0de0e660e6fb05 Swarm Control of UAVs for Cooperative Hunting with DDDAS (2013) ]</ref>
We found an article describing two “cleaning” algorithms for swarms of robots. It explains how these algorithms can be used as “hunting” protocols to, for instance, find lost humans. The first of the two algorithms, Parallel Path Search, works on rectangular areas only and requires them to have no “leakage”, but is computationally quite light. Leakage is a term used to describe the phenomenon of dirt leaking from dirty sectors to clean sectors or, closer to the example, the opportunity for a fugitive to escape when gaps between drones are too large. The second, the SWEEP protocol, on the other hand, will work on any non-disconnected area and can work around "leakage", but is computationally quite expensive at times and requires an overview of the entire area. The DDDAS framework uses the best of Parallel Path Search and the SWEEP protocol to create a better framework, that also takes potential loss of drones into account. All in all, some very interesting algorithms already exist for the theme in mind. <ref name="SwarmControl">[https://ac-els-cdn-com.dianus.libr.tue.nl/S1877050913005796/1-s2.0-S1877050913005796-main.pdf?_tid=97585fa4-0be9-11e8-a6b1-00000aacb35f&acdnat=1517996033_0b095911bc2b3916dd0de0e660e6fb05 Swarm Control of UAVs for Cooperative Hunting with DDDAS (2013) ]</ref>


Another article found describes a framework for multi-agent research. The platform used for the development of this framework were low-cost quadcopters. The demonstration of the platform on environment exploration and collision avoidance showed that the platform is decent. The largest problem is dataloss when using wireless communication over larger distances. This means that the platform still needs improving to make it more robust, consistent and reliable. <ref name="MultiAgentEnvironmentExploration">[https://link-springer-com.dianus.libr.tue.nl/content/pdf/10.1007%2F978-3-319-10401-0.pdf Multi-agent Environment Exploration
Another article found describes a framework for multi-agent research. The platform used for the development of this framework were low-cost quadcopters. The demonstration of the platform on environment exploration and collision avoidance showed that the platform is decent. The largest problem is dataloss when using wireless communication over larger distances. This means that the platform still needs improving to make it more robust, consistent and reliable. <ref name="MultiAgentEnvironmentExploration">[https://link-springer-com.dianus.libr.tue.nl/content/pdf/10.1007%2F978-3-319-10401-0.pdf Multi-agent Environment Exploration with AR.Drones (2014) ]</ref>
with AR.Drones (2014) ]</ref>


== Sensors and analysis methods ==
== Sensors and analysis methods ==

Revision as of 10:14, 12 February 2018

State of the art report for the project of PRE2017 3 Groep17.

Image stitching

Drone flying and controlling

Swarm Technology

We found an article describing two “cleaning” algorithms for swarms of robots. It explains how these algorithms can be used as “hunting” protocols to, for instance, find lost humans. The first of the two algorithms, Parallel Path Search, works on rectangular areas only and requires them to have no “leakage”, but is computationally quite light. Leakage is a term used to describe the phenomenon of dirt leaking from dirty sectors to clean sectors or, closer to the example, the opportunity for a fugitive to escape when gaps between drones are too large. The second, the SWEEP protocol, on the other hand, will work on any non-disconnected area and can work around "leakage", but is computationally quite expensive at times and requires an overview of the entire area. The DDDAS framework uses the best of Parallel Path Search and the SWEEP protocol to create a better framework, that also takes potential loss of drones into account. All in all, some very interesting algorithms already exist for the theme in mind. [1]

Another article found describes a framework for multi-agent research. The platform used for the development of this framework were low-cost quadcopters. The demonstration of the platform on environment exploration and collision avoidance showed that the platform is decent. The largest problem is dataloss when using wireless communication over larger distances. This means that the platform still needs improving to make it more robust, consistent and reliable. [2]

Sensors and analysis methods

The first article belonging to the category presents a list of different sensors and camera's that are currently available for drone flight and sectors where they are used often. These consist of, but are not limited to, accellerometers, cameras (both infrared and normal) and GPS.[3]

Another article we discovered shows the development of imaging spectroscopy in multi-temporal environments using a multitude of drones. In layman words, a way to perform analysis on a series of high-resolution photos that have been taken from different directions. This technique is quite promising and seems to perform equally to or better than other techniques.[4]

A third article found discussed the evolution of photogrammetric and remote sensing technologies in the field of Unmanned Aerial Systems. It shows that drones are ready for use, but that large regulatory issues still have to be dealt with. However the European Union has hatched a multi-annual plan to integrate the use of drones for mapping, which would remove most current issues. It also shows that the navigation systems are generally ready, but improvement is still possible. Sensors have been developed to the point that they are small and lightweight enough to be used on drones. Additionally, the market of interested parties is growing with the day. In conclusion: additional research and technologies are defininitely required and are being conducted. We will definitely see more new UAS technologies and applications in photogrammetry and remote sensing in coming years.[5]

== References ==