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.
About Lori Cameron
Lori Cameron is a Senior Writer for the IEEE Computer Society and currently writes regular features for Computer magazine, Computing Edge, and the Computing Now and Magazine Roundup websites. Contact her at email@example.com. Follow her on LinkedIn.