Robust Tracking of Soccer Robots Using Random Finite Sets

Abstract

Maintaining a good estimation of the other robots’ positions is crucial in soccer robotics, as in most multirobot systems applications. Classical approaches use a vector representation of the robots’ positions and Bayesian filters to propagate them over time. However, these approaches suffer from the data association problem. To tackle this issue, this article presents a new methodology for the robust tracking of robots based on the Random Finite Sets framework, which doesn’t require any explicit data association. Moreover, the proposed methodology is able to integrate information shared by teammate robots, their positions, and their estimations of the other robots’ positions. The robots’ tracking is based on the use of a GM-PHD filter, where the estimations of the robots’ positions and observations are represented using mixture of Gaussians, but instead of associating a robot’s hypothesis or an observation to a given Gaussian, the weight of each Gaussian maintains an estimation of the number of robots that it represents. The methodology is validated in several soccer matches and compared with a classical multihypothesis EKF tracking methodology. The proposed method is able to reduce the errors of the estimated robots’ positions in about 35 percent.