Cooperative Robot Localization Using Event-triggered Estimation.
This paper describes a novel communication-spare cooperative localizationalgorithm for a team of mobile unmanned robotic vehicles. Exploiting anevent-based estimation paradigm, robots only send measurements to neighborswhen the expected innovation for state estimation is high. Since agents knowthe event-triggering condition for measurements to be sent, the lack of ameasurement is thus also informative and fused into state estimates. The robotsuse a Covariance Intersection (CI) mechanism to occasionally synchronize theirlocal estimates of the full network state. In addition, heuristic balancingdynamics on the robots' CI-triggering thresholds ensure that, in large diameternetworks, the local error covariances remains below desired bounds across thenetwork. Simulations on both linear and nonlinear dynamics/measurement modelsshow that the event-triggering approach achieves nearly optimal stateestimation performance in a wide range of operating conditions, even when usingonly a fraction of the communication cost required by conventional full datasharing. The robustness of the proposed approach to lossy communications, aswell as the relationship between network topology and CI-based synchronizationrequirements, are also examined.
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