Author name cluster

Ivan Krasičenko

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2020 System Paper

MAPF Scenario: Software for Evaluating MAPF Plans on Real Robots

Roman Barták
Jiří Švancara
Ivan Krasičenko

Multi-Agent Path Finding (MAPF) deals with ﬁnding collision free paths for a set of agents (robots) moving on a graph. The interest in MAPF in the research community started to increase recently partly due to practical applications in areas such as warehousing and computer games. However, the academic community focuses mostly on solving the abstract version of the problem (moving of agents on the graph) with only a few results on real robots. The presented software MAPF Scenario provides a tool for specifying MAPF problems on grid maps, solving the problems using various abstractions (for example, assuming rotation actions or not), simulating execution of plans, and translating the abstract plans to control programs for small robots Ozobots. The tool is intended as a research platform for evaluating abstract MAPF plans on real robots and as an educational and demonstration tool bridging the areas of artiﬁcial intelligence and robotics.

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IJCAI Conference 2019 Conference Paper

Multi-Agent Path Finding on Ozobots

Roman Barták
Ivan Krasičenko
Jiří Švancara

Multi-agent path finding (MAPF) is the problem to find collision-free paths for a set of agents (mobile robots) moving on a graph. There exists several abstract models describing the problem with various types of constraints. The demo presents software to evaluate the abstract models when the plans are executed on Ozobots, small mobile robots developed for teaching programming. The software allows users to design the grid-like maps, to specify initial and goal locations of robots, to generate plans using various abstract models implemented in the Picat programming language, to simulate and to visualise execution of these plans, and to translate the plans to command sequences for Ozobots.

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AAMAS Conference 2019 Conference Paper

Multi-Agent Path Finding on Real Robots

Roman Barták
Ivan Krasičenko
Jičí Švancara

Multi-agent path finding (MAPF) deals with the problem of finding a collision-free path for a set of agents in a graph. It is an abstract version of the problem to coordinate movement for a set of mobile robots. This demo presents software guiding through the MAPF task, starting from the problem formulation and finishing with execution of plans on real robots. Users can design grid-like maps, specify initial and goal locations of robots, generate plans using various abstract models implemented in the Picat programming language, simulate and visualize execution of these plans, and translate the plans to command sequences for Ozobots, small robots developed for teaching programming.

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