With the development of factory automation and intelligent manufacturing system technology, an autonomous mobile robot (AMR) system has become an essential part of controlling the logistics management system within a facility. Relevant research about AMR path planning usually focuses on the fully autonomous environment that does not consider the uncertainty of human behavior. The behavior of human operators is unpredictable and therefore difficult to be integrated into the AMR system path planning analysis. In this paper, we propose an optimization algorithm for improving the pre-planned path considering the uncertainty of human behavior. Conditional value-at-risk constraints and chance constraints are considered in the optimization algorithm as the risk measurement to guarantee safety of operation. The performance of our approach is demonstrated through a 2-D AMR simulation, and the comparison of these two different risk measurements and their performance is also discussed.