Updated Weight Graph for dynamic path planning of multi-AGVs in healthcare environments

Abstract

Automated Guided Vehicle (AGV) is the key factor to improve favorable logistics solutions for human supply chains. One of the most difficult problems of controlling AGV in a large-scale human-aware environment is the uncertainty of transportation. This uncertainty boosts demand for a graph-based model that reflects not only transportation layout but also traffic situations. Given this graph, our algorithmic solution updates weights of the graph over time as well as predicts any congestion ahead of AGVs. We validate our approach by a simulation model in Omnet++/Veins/SUMO and the results show that dynamic path planning allows AGVs to bypass both the ongoing and forthcoming traffic jams.