A Study of Time-varying Cost Parameter Estimation Methods in Automated Transportation Systems based on Mobile Robots
This addresses the need for accurate real-time control in automated transportation systems, but it is incremental as it investigates existing filtering methods for a known bottleneck.
This work tackled the problem of on-line cost parameter estimation for automated guided vehicles, focusing on time-varying factors like battery levels and floor conditions, and found that these parameters significantly impact system performance compared to static computations.
Control of systems of automated guided vehicles involves action planning at many levels. For efficient control of these systems, accurate estimation of cost parameters (speed, energy, task completion performance, \textit{et~cetera} is required. These parameters change along time, particularly in battery-operated robots, which are very sensitive to battery level variations. This work addresses the problem of on-line cost parameter identification and estimation for proper control decisions of the individual mobile robots and for the system as a whole. Several filtering and estimation methods have been investigated with respect to travelling times, which are dramatically affected by battery charges and condition of facility's floors, among other factors. Results show that these parameters depend on the robot, the route and the moment, so they are linked to a particular robot, a region of the floor and a time period (or to a battery level). Moreover, differences with static, pre-runtime travelling time computations, either heuristically or by characterization of real robots, are large enough to affect to system's performance and overall productivity and efficiency.