Moving Target Tracking with a Mobile Robot based on Modified Social Force Model

Abstract

A target tracking approach is proposed for mobile robots in this paper to address the human-robot coexistence and collaboration problem. The improved social force model (SFM) is applied to improve the tracking performance of the robot in crowded environments. When the robot approaches the pedestrians or obstacles, the tracking strategy is adaptively adjusted to avoid collision. The inverse reinforcement learning (IRL) is used to learn the parameters of the improved SFM, where the training data for the IRL is collected in real-world scenes. An effective criterion is designed to evaluate the tracking performance, which fully considers the relationship between the robot and surrounding environments. The experimental results demonstrate the effectiveness of the proposed target tracking method.