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

Efficient control of systems based on automated guided vehicles requires accurate estimation of cost parameters such as speed, energy, task completion performance, et cetera. These parameters change along time, particularly in battery-operated robots. This work addresses the problem of on-line cost parameter identification and estimation which will be beneficial 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, as they are considered key cost parameters. 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). In fact, differences with static, pre-runtime travelling time computations, either heuristically or by characterization of real robots, are large enough to affect system's performance and overall productivity and efficiency.