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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Multi-agent Path Finding
 Švancara, Jiří ; Barták, Roman (advisor)
Multi-Agent Path Finding (MAPF) is the task to find efficient collision-free paths for a fixed set of agents. Each agent moves from its initial location to its desired destination in a shared environment represented by a graph. The classical definition of MAPF is very simple and usually does not reflect the real world accurately. In this thesis, we try to add several attributes to the MAPF definition so that we overcome this shortcoming. This is done in several steps. First, we present an approach on how to model and solve MAPF via reduction to Boolean satisfiability using Picat programming language. This provides us with a useful model that can be easily modified to accommodate additional constraints. Secondly, we modify MAPF to portray a more realistic world. Specifically, we allow new agents to enter the shared environment during the execution of the found plan, and we relax the requirement on the homogeneousness of the shared environment. Lastly, we experimentally verify the applicability of the novel models on real robots in comparison with the classical MAPF setting.
Detailed record
Multi-agent Path Finding
 Švancara, Jiří ; Barták, Roman (advisor) ; Koenig, Sven (referee) ; Vokřínek, Jiří (referee)
Multi-Agent Path Finding (MAPF) is the task to find efficient collision-free paths for a fixed set of agents. Each agent moves from its initial location to its desired destination in a shared environment represented by a graph. The classical definition of MAPF is very simple and usually does not reflect the real world accurately. In this thesis, we try to add several attributes to the MAPF definition so that we overcome this shortcoming. This is done in several steps. First, we present an approach on how to model and solve MAPF via reduction to Boolean satisfiability using Picat programming language. This provides us with a useful model that can be easily modified to accommodate additional constraints. Secondly, we modify MAPF to portray a more realistic world. Specifically, we allow new agents to enter the shared environment during the execution of the found plan, and we relax the requirement on the homogeneousness of the shared environment. Lastly, we experimentally verify the applicability of the novel models on real robots in comparison with the classical MAPF setting.
Detailed record

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