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Mobile robots exploration through cnn-based reinforcement learning

Overview of attention for article published in Robotics and Biomimetics, December 2016
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Mentioned by

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2 tweeters

Citations

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34 Dimensions

Readers on

mendeley
63 Mendeley
Title
Mobile robots exploration through cnn-based reinforcement learning
Published in
Robotics and Biomimetics, December 2016
DOI 10.1186/s40638-016-0055-x
Pubmed ID
Authors

Lei Tai, Ming Liu

Abstract

Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Student > Bachelor 13 21%
Student > Master 7 11%
Professor 4 6%
Researcher 4 6%
Other 10 16%
Unknown 11 17%
Readers by discipline Count As %
Computer Science 24 38%
Engineering 23 37%
Business, Management and Accounting 2 3%
Social Sciences 2 3%
Psychology 1 2%
Other 0 0%
Unknown 11 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 January 2017.
All research outputs
#6,669,763
of 11,222,218 outputs
Outputs from Robotics and Biomimetics
#8
of 29 outputs
Outputs of similar age
#160,517
of 317,024 outputs
Outputs of similar age from Robotics and Biomimetics
#1
of 2 outputs
Altmetric has tracked 11,222,218 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29 research outputs from this source. They receive a mean Attention Score of 1.2. This one scored the same or higher as 21 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,024 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them