Abstract
The problem of Multi-Agent Path Finding (MAPF) is to find paths for a fixed set of agents from their current locations to some desired locations in such a way that the agents do not collide with each other. This problem has been extensively theoretically studied, frequently using an abstract model, that expects uniform durations of moving primitives and perfect synchronization of agents/robots. In this paper we study the question of how the abstract plans generated by existing MAPF algorithms perform in practice when executed on real robots, namely Ozobots. In particular, we use several abstract models of MAPF, including a robust version and a version that assumes turning of a robot, we translate the abstract plans to sequences of motion primitives executable on Ozobots, and we empirically compare the quality of plan execution (real makespan, the number of collisions).
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Acknowledgement
Roman Barták is supported by the Czech Science Foundation under the project P202/12/G061 and together with Jiří Švancara by the Czech-Israeli Cooperative Scientific Research Project 8G15027. This research was also partially supported by SVV project number 260 453.
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Barták, R., Švancara, J., Škopková, V., Nohejl, D. (2018). Multi-agent Path Finding on Real Robots: First Experience with Ozobots. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_24
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