We consider cognitive factories with multiple teams of heterogenous robots, and address two key challenges of these domains, hybrid reasoning for each team and finding an optimal global plan (with minimum makespan) for multiple teams. For hybrid reasoning, we propose modeling each team’s workspace taking into account capabilities of heterogeneous robots, embedding continuous external computations into discrete symbolic representation and reasoning, not only optimizing the makespans of local plans but also minimizing the total cost of robotic actions. To find an optimal global plan, we propose a semi-distributed approach that does not require exchange of information between teams but yet achieves on an optimal coordination of teams that can help each other. We prove that the optimal coordination problem is NP-complete, and describe a solution using automated reasoners. We experimentally evaluate our methods, and show their applications on a cognitive factory with dynamic simulations and a physical implementation.
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This work is partially supported by Scientific and Technological Research Council of Turkey (TUBITAK) Grant 111E116. Z. G. Saribatur’s work was carried out during her graduate studies at Sabancı University.
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Saribatur, Z.G., Patoglu, V. & Erdem, E. Finding optimal feasible global plans for multiple teams of heterogeneous robots using hybrid reasoning: an application to cognitive factories. Auton Robot 43, 213–238 (2019). https://doi.org/10.1007/s10514-018-9721-x