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Luka Kalinovcic

dblp:48/9964 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%
Artificial intelligence
1 paper
Motion planning and robot control · 33% Multi-agent systems · 33% Planning, search and constraint satisfaction · 33%

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

TopicWeightPapersLastEvidence papers
Computational science and engineering
warehouse automation
0.112011
A control architecture for warehouse automation - Performance evaluation in USARSim · ICRA 2011
Knowledge, reasoning and agents › Multi-agent systems
multi-agent coordination
0.012011
A control architecture for warehouse automation - Performance evaluation in USARSim · ICRA 2011
Robotics › Motion planning and robot control
path planning
0.012011
A control architecture for warehouse automation - Performance evaluation in USARSim · ICRA 2011
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › scheduling
task scheduling
0.012011
A control architecture for warehouse automation - Performance evaluation in USARSim · ICRA 2011

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

layered control architecture · 0.2RFID localization · 0.2
YearPublicationVenuePosition
2011 A control architecture for warehouse automation - Performance evaluation in USARSim
abstract
In this paper, we present a control architecture for fully automated warehouse operation. The scenario we are considering consists of several autonomous guided vehicles (AGVs), transporting packages from an input location to one of several destinations, through an unstructured warehouse environment. The AGVs are equipped with wheel encoders, laser range finders and RFID tag readers. Each package has a RFID tag encoding its destination. A map of the warehouse is provided a priori. We have developed a layered control architecture that enables fully autonomous system operation. Starting with a map of the environment, the control system generates feasible paths, assigns tasks to the vehicles and schedules task execution. At the lowest layer, waypoint tracking controllers enable task execution. The proposed architecture has been successfully tested in a simulated scenario at the IEEE Virtual Manufacturing Automation Competition (VMAC), using the USARSim simulator.
Damjan Miklic, Stjepan Bogdan, Luka Kalinovcic
ICRA3