A Thermodynamic Approach to the Analysis of Multi-Robot Cooperative Localization Under Independent Errors.

We propose a new approach to the simultaneous cooperative localization of a group of robots capa-ble of sensing their own motion and the relative position of nearby robots. In the last decade, the use of distributed optimal Kalman filters (KF) to solve this problem have been studied extensively. In this paper, we propose to use a sub-optimal Kalman filter (denoted by EA). EA requires sig-nificantly less computation and communication re-sources then KF. Furthermore, in some cases, EA provides better localization. In this paper EA is analyzed in a soft “thermody-namic ” fashion i.e. relaxing assumptions are used during the analysis. The goal is not to derive hard lower or upper bounds but rather to characterize the robots expected behavior. In particular, to pre-dict the expected localization error. The predic-tions were validated using simulations. We believe that this kind of analysis can be beneficial in many other cases.