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Control of systems of automated guided vehicles involves action planning atmany levels. For efficient control of these systems, accurate estimation ofcost parameters (speed, energy, task completion performance, \textit{et~cetera}is required. These parameters change along time, particularly inbattery-operated robots, which are very sensitive to battery level variations.This work addresses the problem of on-line cost parameter identification andestimation for proper control decisions of the individual mobile robots and forthe system as a whole. Several filtering and estimation methods have beeninvestigated with respect to travelling times, which are dramatically affectedby battery charges and condition of facility's floors, among other factors.Results show that these parameters depend on the robot, the route and themoment, so they are linked to a particular robot, a region of the floor and atime period (or to a battery level). Moreover, differences with static,pre-runtime travelling time computations, either heuristically or bycharacterization of real robots, are large enough to affect to system'sperformance and overall productivity and efficiency.

A Study of Time-varying Cost Parameter Estimation Methods in Automated Transportation Systems based on Mobile Robots