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Nachimuthu Manickam

dblp:33/1703 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1996
—ORCID · none

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
Reinforcement learning · 50% Robot navigation and mapping · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
exploration
0.011996
Cooperative terrain model acquisition by a team of two or three point-robots · ICRA 1996
Robotics › Robot navigation and mapping › terrain perception
terrain model acquisition
0.011996
Cooperative terrain model acquisition by a team of two or three point-robots · ICRA 1996
Robotics › Robot navigation and mapping
cooperative sensing
0.011996
Cooperative terrain model acquisition by a team of two or three point-robots · ICRA 1996
Machine learning › Reinforcement learning › exploration
multi-robot exploration
0.011996
Cooperative terrain model acquisition by a team of two or three point-robots · ICRA 1996

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

restricted visibility graph · 0.0hierarchical decomposition · 0.0
YearPublicationVenuePosition
1996 Cooperative terrain model acquisition by a team of two or three point-robots
abstract
We address the model acquisition problem for an unknown planar polygonal terrain by a team of two or three point robots. The robots are equipped with visual sensors which acquire all visible parts of the terrain by scan operations executed from their locations. The robots communicate with each other via wireless connection. The performance is measured by the number of the sensor (scan) operations. We employ the restricted visibility graph methods an a hierarchical setup. For terrains with convex obstacles and for teams of n (where n=2, 3) robots, we prove that the sensing time is reduced by a factor of 1/n. For terrains with concave corners, the performance of the algorithm for the n(2, 3) robot team is expressed in terms of the sizes of n-connected components, and the sizes and diameters of (n-1) or less connected components.
Nageswara S. V. Rao, Vladimir A. Protopopescu, Nachimuthu Manickam
ICRA3