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Zharaskhan Kuanyshuly

dblp:246/7771 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
0009-0007-9175-4850ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 100%
Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › tactile sensing › contact sensing
contact localization
0.412019
Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin · ICRA 2019
Robotics › Robot manipulation
tactile sensing
0.412019
Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin · ICRA 2019
Haptics and multimodal interaction › tactile sensing
artificial skin
0.112019
Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin · ICRA 2019

Methods — techniques the papers use, named apart from their topics

hierarchical classification · 0.8classical machine learning · 0.8
YearPublicationVenuePosition
2019 Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin
abstract
The sense of touch is essential for reliable mapping between the environment and a robot which interacts physically with objects. Presumably, an artificial tactile skin would facilitate safe interaction of the robots with the environment. In this work, we present our color-coded tactile sensor, incorporating plastic optical fibers (POF), transparent silicone rubber and an off-the-shelf color camera. Processing electronics are placed away from the sensing surface to make the sensor robust to harsh environments. Contact localization is possible thanks to the lower number of light sources compared to the number of camera POFs. Classical machine learning techniques and a hierarchical classification scheme were used for contact localization. Specifically, we generated the mapping from stimulation to sensation of a robotic perception system using our sensor. We achieved a force sensing range up to 18 N with the force resolution of around 3.6 N and the spatial resolution of 8 mm. The color-coded tactile sensor is suitable for tactile exploration and might enable further innovations in robust tactile sensing.
Zhanat Kappassov, Daulet Baimukashev, Zharaskhan Kuanyshuly, Yerzhan Massalin, Arshat Urazbayev, Huseyin Atakan Varol
ICRA3