The navigation capability of a group of robots can be improved by sensing of relative inter-robot positions and intercommunication of position estimates and planned trajectories. The cooperative navigation system (CNS) algorithm described here is based on a Kalman filter which uses inter-robot position sensing to update the collective position estimates of the group. Assuming independence of sensing and positioning errors, the CNS algorithm always improves individual robot estimates and the collective navigation performance improves as the number of robots increases. The CNS algorithm computation may be distributed among the robot group. Simulation results and experimental measurements on two Yamabico robots are described.
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