A study of Time-varying Cost Parameter Estimation Methods in Traffic Networks for Mobile Robots

Industrial robust controlling systems built using automated guided vehicles (AGVs) requires planning which depends on cost parameters like time and energy of the mobile robots functioning in the system. This work addresses the problem of on-line traversal time identification and estimation for proper mobility of mobile robots on systems’ traffic networks. Several filtering and estimation methods have been investigated with respect to proper identification of traversal time of arcs of systems’ transportation graphs. We have found that traversal times vary along time due to a variety of factors, including the battery charge of the robot, and that the best method to predict the next value must account not only for these but for a high variance. Results show that path planning and navigation for each of the mobile robots can be made much more efficient with this approach.