Generalizations of multi-agent path finding problem for incremental environments

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2022-7-20

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Semiz, Fatih

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Multi-Agent Path Finding problem (MAPF) is finding a path for multiple agents from a list of starting locations to a list of goal locations in such a way that the agents’ routes do not pass through the same location at the same time. The problem occurs in real-world during the transporting packages in warehouse environments with robots moving on rails, cleaning closed areas with cleaning robots, and protecting areas with multiple robots etc. It is usually sufficient to use discrete maps to express these problems. However, unlike the standard MAPF setting, in these problems there may be a need to replan agent paths while the movement of the agents continues. The need to replan agent paths may be due to the following reasons: packages falling on the road, passing of external vehicles, or new tasks added to the problem while the problem continues. Such situations can be better expressed with an incremental MAPF problem structure. In this thesis, we describe a MAPF variation where certain nodes on the map become temporarily impassable. We have created methods that effectively solve this problem definition and have proven through many experiments that they are effective solutions. We also defined a MAPF problem variation in which agents have multiple destinations in the lifelong MAPF problem structure. We have developed a new algorithm that solves the MAPF problem involving multiple destinations. For the task allocation problem, we created heuristic methods to minimize the total amount of travelled paths, and we tested these methods with many experiments and analyzed their performance.

Subject Keywords

Multi-agent path finding, Incremental planning, Task allocation, Heuristic search, Lifelong planning

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https://hdl.handle.net/11511/98581

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

F. Semiz, “Generalizations of multi-agent path finding problem for incremental environments,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.