The paper considers the problem of generating a navigation solution for a robotic system operating under changing environmental conditions, degradation of measurement channels, and incomplete a priori information about the current situation. It is shown that single-mode navigation algorithms with a fixed measurement processing structure do not always provide the required state estimation quality when the availability and reliability of navigation sources change. An adaptive navigation method based on discrete switching of operating modes depending on an integral uncertainty estimate is proposed. Within the method, diagnostic features are formed to characterize the consistency of measurements with the predicted state, the level of estimation uncertainty, and the structural availability of navigation channels. Based on these features, an integral uncertainty index is calculated and used to select one of the predefined processing modes: nominal data fusion, partial degradation of measurement information, or limited correction. To prevent false transitions, the selected mode is confirmed over several consecutive steps. Numerical simulation results show that the proposed approach reduces the influence of degraded measurements on state estimation and decreases the position error compared with a base line single-mode algorithm.