Learning the motion patterns of humans for predictive navigation

To achieve fully autonomous mobile robot in crowded environments, an efficient and real-time motion planning is necessary. In this paper, an A*-based predictive motion planner is presented for navigation tasks. A generalized pedestrian motion model is also introduced in this paper. By understanding pedestrian motion patterns, the robot can further predict their motions and avoid the collision as early as possible. The simulations and experiments are also shown to validate the idea of this paper.