Evidence map›Paper›PMID 42620899›Full record

ReviewFrontiers in robotics and AI2026

Robots in mine search and rescue operations: a review of platforms and design requirements.

R Bakzadeh, V Androulakis, H Khaniani, S Shao, M Hassanalian, P Roghanchi

Abstract readReview
In one paragraph

Review in Frontiers in robotics and AI, 2026. 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

6 authors.

R BakzadehDepartment of Mining Engineering, University of Kentucky, Lexington, KY, United States.
V AndroulakisSenseLab Space Informatics, Technical University of Crete, Chania, Crete, Greece.
H KhanianiDepartment of Mineral Engineering, New Mexico Tech, Socorro, NM, United States.
S ShaoDepartment of Electrical Engineering, Colorado School of Mines, Golden, CO, United States.
M HassanalianDepartment of Mechanical Engineering, New Mexico Tech, Socorro, NM, United States.
P RoghanchiDepartment of Mining Engineering, University of Kentucky, Lexington, KY, United States.

Funding

NIOSH CDC HHS U60 OH012351
6 · The paper itself

Abstract

The use of automation in mining can be found at all stages of the mining process, covering exploration, excavation, loading, transportation, mineral processing, and search and rescue missions in emergency situations. Due to the harsh conditions during an underground mine disaster, robots can be of great assistance to rescue teams by entering areas that are unsafe for human rescuers, locating trapped workers, and collecting valuable data. The design and implementation of coal-mine rescue robots are characterized by great complexity because they encompass a great range of components and requirements. However, comprehensive guidelines for the design and deployment of robots in harsh underground environments have not been established. As a result, the developers of coal-mine rescue robots (CMRRs) must exercise their own judgment on the development and evaluation of the platform functionalities. This survey attempts to review design and functionality requirements based on the common practices and lessons learned from the existing CMRRs with the aim of facilitating future research and development on coal-mine search and rescue robots. An updated overview of the documented CMRRs of the last 3 decades showcases the intricate relationship of the common design considerations for CMRR design.

Indexed as

coal-mine rescue robotsdesign requirementsmine emergenciesmine search and rescue operationsrobotics

Identifiers

PMID42620899
PMCPMC13487453

What OpenQuestion holds

Textmetadata
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.