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*...Show MoreMetadata
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.
Published in: 2020 Winter Simulation Conference (WSC)
Date of Conference: 14-18 December 2020
Date Added to IEEE Xplore: 29 March 2021
ISBN Information: