Evidence map›Paper›PMID 35009919›Full record

ArticleSensors (Basel, Switzerland)2022

Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network.

Ciprian Lapusan, Olimpiu Hancu, Ciprian Rad

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

3 authors.

Ciprian LapusanDepartment of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0003-0995-6220
Olimpiu HancuDepartment of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0002-1108-3062
Ciprian RadDepartment of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0003-3511-107X

Funding

Romanian Ministry of Education and Research PN-III-P2-2.1-PED-2019-4939
6 · The paper itself

Abstract

The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.

Indexed as

RoboticsBiomechanical PhenomenaFeedbackIMU sensorrobot shape sensingsensor network

Identifiers

PMID35009919
PMCPMC8749592

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.