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(searched for: doi:10.29252/ijmt.11.1)
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Maojia P. Li, Michael E. Kuhl, Rashmi Ballamajalu, Clark Hochgraf, Raymond Ptucha, Amlan Ganguly, Andres Kwasinski
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.
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