No abstract

Some problems associated with building map in confined environments for mobile robots are studied in this work. The uncertainties arising from specular reflection of ultrasonic sensors cannot make mobile robots recognize their surrounding environments correctly when mobile robots navigate in a confined environment. If the environment is known and unchanged, its map can be built with ultrasonic sensors in advance before navigation is performed. This map can provide reliable information about the environment when mobile robots work. A multi-layer fusion algorithm is proposed for building the map in advance. For the situation that the environment is unknown or variable, an adaptive ultrasonic sensor model is applied to build map dynamically. The experiment results indicate that the above fusion algorithms improve the performance of ultrasonic sensors.

In this paper, to reduce the computation load of federated Kalman filters, a simplified federated filtering algorithm for integrated navigation systems is presented. It has been known that the per-cycle computation load grows roughly in proportion to the number of states and measurements for a single centralized Kalman filter. Hence, the states that have poor estimation accuracies are removed from local filters, so that the per-cycle computation load is reduced accordingly. Local filters and master filter of the federated Kalman filter may have different states, so the transition matrices are required to combine the outputs from the local filters and the master filter properly and to reset the global solution into the local filters and the master filter correctly. An experiment demonstrates that the proposed algorithm effectively reduces the computation load, compared with the standard federated Kalman filtering algorithm.

No abstract

No abstract