Evidence map›Paper›PMID 34450801›Full record

ArticleSensors (Basel, Switzerland)2021

A Flexible Multimodal Sole Sensor for Legged Robot Sensing Complex Ground Information during Locomotion.

Yingtian Xu, Ziya Wang, Wanjun Hao, Wenyu Zhao, Waner Lin, Bingchen Jin, Ning Ding

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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
0.7field-weighted citation impact, top 35% of its field
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, 13 citations in OpenAlex.

  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

7 authors at 1 institution in 1 country.

Yingtian XuShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.
Ziya WangShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.ORCID 0000-0002-9582-8139
Wanjun HaoShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.
Wenyu ZhaoShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.
Waner LinShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.
Bingchen JinShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.
Ning DingShenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.
Shenzhen Academy of Robotics · CN

Funding

National Key R&D Program of China 2019YFB1310403National Natural Science Foundation of China 61903317Shenzhen Institute of Artificial Intelligence and Robotics for Society AC01202101011
6 · The paper itself

Abstract

Recent achievements in the field of computer vision, reinforcement learning, and locomotion control have largely extended legged robots' maneuverability in complex natural environments. However, little research focuses on sensing and analyzing the physical properties of the ground, which is crucial to robots' locomotion during their interaction with highly irregular profiles, deformable terrains, and slippery surfaces. A biomimetic, flexible, multimodal sole sensor (FMSS) designed for legged robots to identify the ontological status and ground information, such as reaction force mapping, contact situation, terrain, and texture information, to achieve agile maneuvers was innovatively presented in this paper. The FMSS is flexible and large-loaded (20 Pa-800 kPa), designed by integrating a triboelectric sensing coat, embedded piezoelectric sensor, and piezoresistive sensor array. To evaluate the effectiveness and adaptability in different environments, the multimodal sensor was mounted on one of the quadruped robot's feet and one of the human feet then traversed through different environments in real-world tests. The experiment's results demonstrated that the FMSS could recognize terrain, texture, hardness, and contact conditions during locomotion effectively and retrain its sensitivity (0.66 kPa

Indexed as

RoboticsTouch PerceptionBiomimeticsHumansLocomotionflexible sensorlegged robotmultimodal haptic perception

Identifiers

PMID34450801
PMCPMC8399010
OpenAlexW3188501713

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.