Maintaining a good estimation of the other robots’ positions is crucial in soccer robotics, as in most multirobot systems applications, according to Pablo Cano and Javier Ruiz-del-Solar of the AMTC Center at Universidad de Chile, authors of “Robust Tracking of Soccer Robots Using Random Finite Sets,” (login may be required for full text) which appears in the November/December 2017 issue of IEEE Intelligent Systems.
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, the authors present 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 proposed method is able to reduce the errors of the robots’ estimated positions by about 35 percent.
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