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S. Peng

dblp:02/4333 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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
Legged, aerial and field robots · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots › legged robots
legged robot locomotion
0.012003
A biologically inspired four legged walking robot · ICRA 2003
Robotics › Legged, aerial and field robots › legged robots › legged robot locomotion
quadruped walking
0.012003
A biologically inspired four legged walking robot · ICRA 2003
Robotics › Legged, aerial and field robots
rough terrain locomotion
0.012003
A biologically inspired four legged walking robot · ICRA 2003

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

subsumption architecture · 0.0central pattern generator · 0.0
YearPublicationVenuePosition
2014 Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles
Eric W. T. Ngai, S. Peng, Paul Alexander, Karen Ka-Leung Moon
Expert Syst. Appl.2
2003 A biologically inspired four legged walking robot
abstract
This paper presents the design and implementation of a biologically inspired four legged walking robot, with a certain level of comparable complexities and similarities to its biological counterpart. A four-phase walking strategy inspired from four legged animals has been proposed and implemented in the robot. Four parallel Subsumption Architectures and a simple Central Pattern Generator are used in the robot for physical implementation. Experimental results demonstrate that the robot employs the proposed walking strategy and can successfully carry out its walking behaviours under various experimental terrain conditions, such as flat ground, incline, decline and uneven ground.
S. Peng, C. P. Lam, G. R. Cole
ICRA1