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Yam Geva

dblp:117/2132 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 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 · 60% Motion planning and robot control · 40%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › motion planning › legged locomotion planning
foothold planning		 0.112012
A combined potential function and graph search approach for free gait generation of quadruped robots · ICRA 2012
Robotics › Legged, aerial and field robots › legged robots › legged robot locomotion
free gait generation		 0.112012
A combined potential function and graph search approach for free gait generation of quadruped robots · ICRA 2012
Robotics › Legged, aerial and field robots
legged robots		 0.112012
A combined potential function and graph search approach for free gait generation of quadruped robots · ICRA 2012
Robotics › Motion planning and robot control
motion planning		 0.112012
A combined potential function and graph search approach for free gait generation of quadruped robots · ICRA 2012
Robotics › Legged, aerial and field robots › legged robots › legged robot locomotion
quadruped locomotion		 0.112012
A combined potential function and graph search approach for free gait generation of quadruped robots · ICRA 2012

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

graph search · 0.1artificial potential field · 0.1
YearPublicationVenuePosition
2012 A combined potential function and graph search approach for free gait generation of quadruped robots
abstract
This paper presents an algorithm for planning the foothold positions of quadruped robots on irregular terrain. The input to the algorithm is the robot kinematics, the terrain geometry, a required motion path, as well as initial posture. Our goal is to develop general algorithm that navigate quadruped robots quasi-statically over rough terrain, using an APF (Artificial Potential Field) and graph searching. The algorithm is planning a sequence set of footholds that navigates the robot along the required path with controllable motion characteristics. Simulations results demonstrate the algorithm in a planner environment.
Yam Geva, Amir Shapiro
 ICRA1