Evidence map›Paper›PMID 33806854›Full record

ArticleSensors (Basel, Switzerland)2021

Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning.

David J Lary, David Schaefer, John Waczak, Adam Aker, Aaron Barbosa, Lakitha O H Wijeratne, Shawhin Talebi, Bharana Fernando, John Sadler, Tatiana Lary and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

David J LaryHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.ORCID 0000-0003-4265-9543
David SchaeferHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
John WaczakHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
Adam AkerHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
Aaron BarbosaHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
Lakitha O H WijeratneHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.ORCID 0000-0003-2688-648X
Shawhin TalebiHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.ORCID 0000-0002-9841-6703
Bharana FernandoHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
John SadlerHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
Tatiana LaryHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
Matthew D LaryHanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.

Funding

SOFWERX Machine Learning for Robotic TeamThe Texas National Security Network Excellence Fund Environmental Sensing Security Sentinels
6 · The paper itself

Abstract

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed for our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability.

Indexed as

autonomoushyper-spectral imagingmachine learningrobotic boatrobot teamUAV

Identifiers

PMID33806854
PMCPMC8004590

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.