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

TL;DR

This paper investigates online methods for estimating time-varying cost parameters in automated guided vehicle systems, highlighting their importance for system efficiency and the impact of dynamic factors like battery level and floor conditions.

Contribution

It evaluates various filtering and estimation techniques for real-time parameter identification, demonstrating their significance over static pre-computed values for system performance.

Findings

Parameters depend on robot, route, and time

Dynamic estimation improves system efficiency

Static estimates are significantly less accurate

Abstract

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.