Evidence map›Paper›PMID 40410334›Full record

ArticleCommunications engineering2025

Low power, non-intrusive 3D localization for underwater mobile robots.

Suryansh Sharma, Daniel Van Passen, R Venkatesha Prasad, Kaushik Chowdhury

Abstract read
In one paragraph

Article in Communications engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Suryansh Sharma *Networked Systems Group, Delft University of Technology, Delft, Netherlands. suryansh@evilscientist.cc.ORCID http://orcid.org/0000-0001-7534-6848
Daniel Van Passen *Networked Systems Group, Delft University of Technology, Delft, Netherlands.
R Venkatesha PrasadNetworked Systems Group, Delft University of Technology, Delft, Netherlands.ORCID http://orcid.org/0000-0001-7443-7641
Kaushik ChowdhuryDepartment of Electrical & Computer Engineering, The University of Texas at Austin, Austin, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autonomous Underwater Vehicles (AUVs) face persistent challenges in localization compared to their counterparts on the ground due to limitations with methods like Global Positioning System (GPS). We propose a novel system for localization, Pisces, that leverages the Angle of Arrival (AoA) and Received Signal Strength Ratio (RSSR) of robot-mounted blue LED signals. This method provides a spectrally efficient training-free solution for estimating 3D underwater positions. The system remains effective despite high water turbidity with a relatively low impact on marine life compared to similar acoustic methods. Pisces is less complex, computationally efficient, and uses less power than camera-based solutions. Pisces enables robust relative localization, especially in swarms of robots with the potential for additional applications like docking. We demonstrate high localization accuracy with a Mean Absolute Error (MAE) of 0.031 m at 0.32 m separation and 0.16 m MAE at 1 m separation. Moreover, it achieved this with minimal power consumption, utilizing only 11 mA of transmitter LED current and performing 3D localization within 10 ms for distances up to 3 m.

Identifiers

PMID40410334
PMCPMC12102152

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.