Evidence map›Paper›PMID 28555030›Full record

ArticleSensors (Basel, Switzerland)2017

A Compact Magnetic Field-Based Obstacle Detection and Avoidance System for Miniature Spherical Robots.

Fang Wu, Akash Vibhute, Gim Song Soh, Kristin L Wood, Shaohui Foong

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. 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. 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.

Fang WuEngineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore. fang_wu@sutd.edu.sg.
Akash VibhuteEngineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore. akash.roboticist@gmail.com.
Gim Song SohEngineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore. sohgimsong@sutd.edu.sg.
Kristin L WoodEngineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore. kristinwood@sutd.edu.sg.
Shaohui FoongEngineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore. foongshaohui@sutd.edu.sg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to their efficient locomotion and natural tolerance to hazardous environments, spherical robots have wide applications in security surveillance, exploration of unknown territory and emergency response. Numerous studies have been conducted on the driving mechanism, motion planning and trajectory tracking methods of spherical robots, yet very limited studies have been conducted regarding the obstacle avoidance capability of spherical robots. Most of the existing spherical robots rely on the "hit and run" technique, which has been argued to be a reasonable strategy because spherical robots have an inherent ability to recover from collisions. Without protruding components, they will not become stuck and can simply roll back after running into bstacles. However, for small scale spherical robots that contain sensitive surveillance sensors and cannot afford to utilize heavy protective shells, the absence of obstacle avoidance solutions would leave the robot at the mercy of potentially dangerous obstacles. In this paper, a compact magnetic field-based obstacle detection and avoidance system has been developed for miniature spherical robots. It utilizes a passive magnetic field so that the system is both compact and power efficient. The proposed system can detect not only the presence, but also the approaching direction of a ferromagnetic obstacle, therefore, an intelligent avoidance behavior can be generated by adapting the trajectory tracking method with the detection information. Design optimization is conducted to enhance the obstacle detection performance and detailed avoidance strategies are devised. Experimental results are also presented for validation purposes.

Indexed as

magnet assemblyminiature robotobstacle avoidanceobstacle detectionsensor optimizationspherical robot

Identifiers

PMID28555030
PMCPMC5492687

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.