Proceedings of the 16th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2021F18

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 153–162 (2021)

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Abstract. We deal here with a fleet of autonomous vehicles, devoted to internal logistics inside some protected area. This fleet is ruled by a hierarchical supervision architecture, which, at the top level distributes and schedules the tasks, and, at the lowest level, ensures local safety. We focus here on the top level, while introducing a time dependent estimation of the risk induced by the traversal of any arc. We set a model, state structural results, and design a bi-level algorithm and a A* routing/scheduling algorithms which both aim at a well-fitted compromise between speed and risk and rely on reinforcement learning.

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