A time-based global path planning strategy for crowd navigation

In a competent crowd navigation system, it is very important for the agents in the system to plan their movements being aware of the other agents. In this study, we propose the use of machine learning methods to create time-based global path plans by utilizing the information as to when and where the other agents would be at a future time. The application of a machine learning method in the traditional manner for the global path planning problem is not a straightforward task due to the complexity of data collection; therefore, this study proposes a novel method to apply machine learning methods for global path planning. This enables us to create a context-free model. We organize experiments to compare our method to a recent and competitive approach that is referred to as the potential-based method (PBM). We employed three different machine learning methods, namely, artificial neural networks, polynomial regression, and support vector regression. The results of the mass scenario tests and a corridor scenario indicate that the versions with polynomial regression and support vector regression outperform the PBM. This encourages further investigations on the use of machine learning methods for global path planning in crowd navigation.