Evidence map›Paper›PMID 39678385›Full record

ReviewHeliyon2024

Next generation legged robot locomotion: A review on control techniques.

Swapnil Saha Kotha, Nipa Akter, Sarafat Hussain Abhi, Sajal Kumar Das, Md Robiul Islam, Md Firoj Ali, Md Hafiz Ahamed, Md Manirul Islam, Subrata Kumar Sarker, Md Faisal Rahman Badal and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

13 authors.

Swapnil Saha KothaDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Nipa AkterDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Sarafat Hussain AbhiDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Sajal Kumar DasDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Md Robiul IslamDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Md Firoj AliDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Md Hafiz AhamedDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Md Manirul IslamDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Subrata Kumar SarkerDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Md Faisal Rahman BadalDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Prangon DasDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Zinat TasneemDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Md Mehedi HasanDepartment of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The next generation of autonomous-legged robots will herald a new era in the fields of manufacturing, healthcare, terrain exploration, and surveillance. We can expect significant progress in a number of industries, including inspection, search and rescue, elderly care, workplace safety, and nuclear decommissioning. Advanced legged robots are built with a state-of-the-art architecture that makes use of stereo vision and inertial measurement data to navigate unfamiliar and challenging terrains. However, designing controllers for these robots is a difficult task due to a number of factors, including dynamic terrains, tracking delays, inaccurate 3D maps, unforeseen events, and sensor calibration issues. To address these challenges, this paper discusses the current methods for controlling autonomous-legged robots. Our primary contribution is comparative research on robot control strategies such as virtual model control (VMC), model predictive control (MPC), and model-free reinforcement learning (RL). This paper provides information on different strategies for controlling autonomous legged robots and discusses the potential advancements and applications of this technology in the future. The aim of this study is to assist future researchers in making informed decisions on the selection of optimal control strategies and innovative concepts when developing and working with legged robots.

Indexed as

AICoMControlLegged robotsLocomotionNext generation

Identifiers

PMID39678385
PMCPMC11639468

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.