Revision 787506187 of "ENav-navigation system" on enwiki

{{Orphan|date=June 2016}}

{{DISPLAYTITLE:eNav-navigation system}}
{{Infobox website
 | name = eNav
 | url = {{URL|http://enav.embedded.rwth-aachen.de}}
 | logo = [[File:ENav-Logo.svg|200px|zentriert]]
 | type = [[Route planning software]]
 | language = 9 languages
 | launch_date = 2016
 | owner = [[RWTH Aachen University]]
 | current_status = Active
}}
[[file:ENav-Planner.png|thumbnail]]
The project '''eNav – A navigation system for electric wheelchairs''' uses several distinct methods and ideas concerning embedded systems to better measure as well as utilize the battery capacity of [[Motorized wheelchair|electric wheelchairs]]. Furthermore, barriers are detected, which can then be avoided. The hallmark of the navigation system is the calculation of an energy efficient route, which is conducted additionally to the calculation of the shortest route. The user has the option to choose between both. eNav can be accessed as a [[route planner]] using a web browser. In the near future,{{When|date=June 2016}} a free navigation app should be available in the [[Google Play|Google Play Store]].

eNav is a project at the [[RWTH Aachen]], which has been launched by the [[informatics]] chair I11 to increase the life quality of people with an impairment.

== Idea ==
The initial idea for eNav originated because of the lack of support from present Navigation systems for electric wheelchair users. A common navigation system can neither provide information about the incline of the to be traversed route, nor can it estimate whether the planned route can be overcome with the current battery level. Inspired by the project RollStuhlrouting<ref>MÜLLER, Astrid, et al. Ein Routenplaner für Rollstuhlfahrer auf der Basis von OpenStreetMap-Daten-Konzeption, Realisierung und Perspektiven. Angewandte Geoinformatik, 2010. ([https://www.vde-verlag.de/proceedings-en/537495033.html Full text])</ref> an idea originated, to specify an according navigation-system. Because of the fact that battery level indicators of electric wheelchairs are inaccurate and unreliable, the motivation arises to develop a system with improved battery level detection. Additionally, emerging technologies should be used to calculate an energy efficient route by consideration of topographical characteristics. The user of the navigation system can choose between the shortest and an energy efficient route.

== Map-data ==
[[file:Kartenquellen.png|thumb|Map layers]]
For the calculation of the energy-efficient route, a [[Cartesian coordinate system|3D]]-map with road surface information is required for the eNav-system. Furthermore, information about accessibility is required to assure 
practicability of the calculated route for an electric wheelchair user. Additionally, the accessibility information of individual buildings ([[Point of Interest|POI]]) is of interest. The map-data of eNav consists of four layers to warrant all these map-properties.

=== 1. OpenStreetMap ===
The first layer is formed by [[OpenStreetMap]] as basis. From it, the complete road network is extracted. Moreover, information about accessibility can be directly extracted or derived from other information like, for example, stairs or ramps.

=== 2. Laserscan ===
For the second layer, the [[Bezirksregierung|district government]] of [[Köln|Cologne]] provided [[Lidar|Laserscan-data]].<ref>[http://www.bezreg-koeln.nrw.de/brk_internet/geobasis/hoehenmodelle/index.html District government of cologne about ALS]</ref> The accuracy of this data is ±20&nbsp;cm. The data provides a possibility to create a three-dimensional map for the system. The incline of a road can be derived from the [[Cartesian coordinate system|three-dimensional coordinates]] of the map. The incline has a significant influence on the energy consumption of an electric wheelchair.<ref>DŽAFIĆ, Dzenan, et al. Modifikation des A*-Algorithmus für energieeffizientes 3D-Routing. 2013. ([http://gispoint.de/fileadmin/user_upload/paper_gis_open/537533019.pdf Full text])</ref>

=== 3. Route surface ===
Route surface information forms the third layer. These are partly enriched by the information provided by the [[Städteregion Aachen]]. This information contains accurate details about the surface of a road, for example, the surface is [[asphalt]] or [[cobblestone]]. Moreover, by using [[crowd-sourcing]] or more specifically  [[Volunteered geographic information]] (VGI) and Contributed Geographical Information (CGI), road surface information is permanently accumulated and incorporated. This is done by measuring the vertical acceleration of an [[accelerometer]] of a [[smartphone]]. Based on the measured values, the road surface type can be retrospectively deduced, whether it consisted out of cobblestone or asphalt. By using [[GPS]], an road surface type can be assigned to a traveled route.

=== 4. POI ===
In the last layer a connection with [[Wheelmap.org]] is made, to enable the user to obtain information about [[accessibility]] of buildings during navigation. That means, the POI are indicated to the user as Green (Accessible), Orange (Limited accessible), Red (Not accessible) or grey (unknown). A [[Point of Interest|Point Of Interest]] can be directly selected as a route target.

== Energy-efficient Route ==
[[file:Verbrauchfunktion.png|thumb|upright=1.1|right|Exponential consumption function]]
For routing a [[weighted graph]] is necessary. For calculating the shortest route, the length of the road is used as edge weight. During energy-efficient routing, the [[Electric energy consumption|energy consumption]] of an [[Motorized wheelchair|electric wheelchair]] forms the edge weight.<ref>FRANKE, Dominik, et al. Konzept eines mobilen OSM-Navigationssystems für Elektrofahrzeuge. Angewandte Geoinformatik, 2011, S. 148–157. ([http://gispoint.de/fileadmin/user_upload/paper_gis_open/537508024.pdf Full text])</ref> Currently, the energy consumption of an electric wheelchair is determined by following influencing factors:
* Length of the edge
* [[Slope|Incline]] of the edge
* [[Friction]] on the edge (Road surface) <ref name="Agit2014">DŽAFIĆ, Dženan, et al. Integration von Bodenbelagsinformationen zum energieeffizienten Routen von Elektrorollstühlen. 2014. ([http://gispoint.de/fileadmin/user_upload/paper_gis_open/537543014.pdf Full text])</ref>
From this the following consumption function can be derived:
:<math>Normal consumption \times Length \times 1{,}16^{Incline} \times friction coefficient</math>
Because of the exponential weighting function a special [[A*]]-[[heuristic]] has been developed, which accelerates route calculation.

== Viability ==
[[file:Efficient vs. shortest.png|thumb|200px|right]]
An [[evaluation]] as part of the eNav-Project found, that in 41% of all tested cases a route exists, which is more [[Efficient energy use|energy-efficient]] than the shortest.<ref name="Agit2014" />

== References ==
<references />

[[Category:Automotive electronics]]
[[Category:Wheelchairs]]