
web.archive.org/web/20191004033503/http:/ai.stanford.edu/blog/adaptive-routing
Preview meta tags from the web.archive.org website.
Linked Hostnames
1Thumbnail

Search Engine Appearance
Adaptive Energy-Efficient Routing for Autonomous Vehicles
A team of aerial and terrestrial robots is sent to analyze previously unexplored terrain by taking photographs and soil samples. Only the aerial robots — drones — can obtain high quality images of tall structures. Imagine that, instead of flying directly from the mission hub to their respective destinations, the drones docked on the ground vehicles for segments of their routes and flew off for a short time to capture some data before swiftly docking back on to a (potentially different) vehicle. Drones are more energy-constrained and sensitive to atmospheric disturbances than their terrestrial counterparts. With such coordination, we could improve energy-efficiency and allow for broader coverage and longer missions.
Bing
Adaptive Energy-Efficient Routing for Autonomous Vehicles
A team of aerial and terrestrial robots is sent to analyze previously unexplored terrain by taking photographs and soil samples. Only the aerial robots — drones — can obtain high quality images of tall structures. Imagine that, instead of flying directly from the mission hub to their respective destinations, the drones docked on the ground vehicles for segments of their routes and flew off for a short time to capture some data before swiftly docking back on to a (potentially different) vehicle. Drones are more energy-constrained and sensitive to atmospheric disturbances than their terrestrial counterparts. With such coordination, we could improve energy-efficiency and allow for broader coverage and longer missions.
DuckDuckGo

Adaptive Energy-Efficient Routing for Autonomous Vehicles
A team of aerial and terrestrial robots is sent to analyze previously unexplored terrain by taking photographs and soil samples. Only the aerial robots — drones — can obtain high quality images of tall structures. Imagine that, instead of flying directly from the mission hub to their respective destinations, the drones docked on the ground vehicles for segments of their routes and flew off for a short time to capture some data before swiftly docking back on to a (potentially different) vehicle. Drones are more energy-constrained and sensitive to atmospheric disturbances than their terrestrial counterparts. With such coordination, we could improve energy-efficiency and allow for broader coverage and longer missions.
General Meta Tags
10- titleAdaptive Energy-Efficient Routing for Autonomous Vehicles | SAIL Blog
- titleAdaptive Energy-Efficient Routing for Autonomous Vehicles | The Stanford AI Lab Blog
- charsetutf-8
- viewportwidth=device-width, initial-scale=1, maximum-scale=1
- generatorJekyll v3.8.5
Open Graph Meta Tags
6- og:titleAdaptive Energy-Efficient Routing for Autonomous Vehicles
og:locale
en_US- og:descriptionA team of aerial and terrestrial robots is sent to analyze previously unexplored terrain by taking photographs and soil samples. Only the aerial robots — drones — can obtain high quality images of tall structures. Imagine that, instead of flying directly from the mission hub to their respective destinations, the drones docked on the ground vehicles for segments of their routes and flew off for a short time to capture some data before swiftly docking back on to a (potentially different) vehicle. Drones are more energy-constrained and sensitive to atmospheric disturbances than their terrestrial counterparts. With such coordination, we could improve energy-efficiency and allow for broader coverage and longer missions.
- og:urlhttps://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog/adaptive-routing/
- og:site_nameSAIL Blog
Twitter Meta Tags
5- twitter:titleAdaptive Energy-Efficient Routing for Autonomous Vehicles
- twitter:descriptionWe introduce the problem of real-time routing for an autonomous vehicle that can use multiple modes of transportation through other vehicles in the area. We also propose a scalable and performant planning algorithm for solving such problems.
- twitter:creator@StanfordAI
- twitter:cardsummary
- twitter:imagehttps://web.archive.org/web/20191004041017im_/http://ai.stanford.edu/blog/assets/img/posts/2019-07-18-adaptive-routing/fig1_roads_png.png
Link Tags
13- alternatehttps://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog/feed.xml
- canonicalhttps://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog/adaptive-routing/
- canonicalhttps://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog/adaptive-routing/
- icon/web/20191004041017im_/http://ai.stanford.edu/blog/assets/img/favicon-32x32.png
- icon/web/20191004041017im_/http://ai.stanford.edu/blog/assets/img/favicon-16x16.png
Links
37- https://web.archive.org/web/20191004041017/http://ai.stanford.edu
- https://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog
- https://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog/about
- https://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog/assets/img/posts/2019-07-18-adaptive-routing/fig1_roads_png.png
- https://web.archive.org/web/20191004041017/http://ai.stanford.edu/blog/assets/img/posts/2019-07-18-adaptive-routing/graph_layer_png.png