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This paper deals with a multi-agent path planning problem for tracking humans in order to obtain detail information such like human behavior and characteristics. To achieve this, paths of agents is planned based on similarity between the predicted intensity of humans positions and the agents’ field of view in the future. The positions of humans are predicted by a k-Nearest Neighbor-based method which allows to predict complicated human movements in a real environment. The number of steps of the planned path is determined according to the consistency between the current prediction and the previous prediction of the future human positions to avoid the path planning using less reliable predictions. We conducted computer simulations and results showed that agents can follow human trajectories observed in a real environment, i.e., concourse of a station by our path planning method.

A Path Planning Method for Human Tracking Agents Using k-Nearest Neighbor-based Prediction