Evidence map›Paper›PMID 36772545›Full record

ArticleSensors (Basel, Switzerland)2023

A Strategy for Controlling Motions Related to Sensory Information in a Walking Robot Big Foot.

Ivan Chavdarov, Kaloyan Yovchev, Lyubomira Miteva, Aleksander Stefanov, Dimitar Nedanovski

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

5 authors.

Ivan ChavdarovInstitute of Robotics, Bulgarian Academy of Sciences, Acad. G. Bonchev St, Bl. 1, 1113 Sofia, Bulgaria.ORCID 0000-0002-3978-4821
Kaloyan YovchevInstitute of Robotics, Bulgarian Academy of Sciences, Acad. G. Bonchev St, Bl. 1, 1113 Sofia, Bulgaria.ORCID 0000-0001-7536-2717
Lyubomira MitevaFaculty of Mathematics and Informatics, University of Sofia "St. Kliment Ohridski", 1504 Sofia, Bulgaria.
Aleksander StefanovFaculty of Mathematics and Informatics, University of Sofia "St. Kliment Ohridski", 1504 Sofia, Bulgaria.ORCID 0000-0002-6614-3464
Dimitar NedanovskiFaculty of Mathematics and Informatics, University of Sofia "St. Kliment Ohridski", 1504 Sofia, Bulgaria.

Funding

"Science and Education for Smart Growth 2014 - 2020", Project CoC "Smart Mechatronic, Eco- And Energy Saving Systems And Technologies" BG05M2OP001-1.002-0023
6 · The paper itself

Abstract

Acquiring adequate sensory information and using it to provide motor control are important issues in the process of creating walking robots. The objective of this article is to present control algorithms for the optimization of the walking cycle of an innovative walking robot named "Big Foot". The construction of the robot is based on minimalist design principles-only two motors are used, with which Big Foot can walk and even overcome obstacles. It is equipped with different types of sensors, with some of them providing information necessary for the realization of an optimized walk cycle. We examine two laws of motion-sinusoidal and polynomial-where we compare the results with constant angular velocity motion. Both proposed laws try to find balance between minimizing shock loads and maximizing walking speed for a given motor power. Experimental results are derived with the help of a 3D-printed working prototype of the robot, with the correct realization of the laws of motion being ensured by the use of a PD controller receiving data from motor encoders and tactile sensors. The experimental results validate the proposed laws of motion and the results can be applied to other walking robots with similar construction.

Indexed as

motor controlmovement of sensorswalking robot

Identifiers

PMID36772545
PMCPMC9918930

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.