Risk-Based A*: Simulation Analysis of a Novel Task Assignment and Path Planning Method

Abstract

This paper addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in material handling applications. We introduce risk-based A*, a novel TAPP method, that aims to reduce conflict and travel distance for AMRs considering system uncertainties such as travel speed, turning speed, and loading/unloading time. An environment simulator predicts the distribution of future locations for each AMR and constructs a probability map for future AMR locations. A revised A* algorithm generates low-risk paths based on the probability map. A discrete event simulation experiment shows our model significantly reduces the number of conflicts among robots in stochastic systems.