Simulation of Mobile Robot Delivery System with Dynamic Bayesian Networks

Abstract

Delivery systems with mobile robots have become increasingly popular due to their efficiency in various industries. However, the complexity of such systems demands advanced simulation techniques for performance evaluation and optimization. Current research on this topic mainly focuses on static models, overlooking the dynamic nature of real-world environments. This paper addresses this gap by proposing a novel approach using Dynamic Bayesian Networks to simulate mobile robot delivery systems. The innovative aspect of this work lies in modeling the dynamic interactions between robots, obstacles, and tasks, leading to more accurate and adaptable system performance predictions. The study contributes to the advancement of delivery system simulation technologies, offering new insights into the design and operation of mobile robot delivery systems.