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The purpose of this paper is to describe research to enable a robust navigation of guide robots in erratic environments with partial sensor information.

Two techniques were developed. One is a robust node discrimination method by using an adaptive sensor matching method. The other is a robot navigation technique with partial sensor information.

A successful navigation was implemented in erratic environments using partial sensor information.

First robot navigation is addressed along the generalized Voronoi graph (GVG) with partial sensor information. A solution is also provided for a phantom node detection problem, which is one of the main defects in GVG navigation.

The paper's purpose is to propose a localization algorithm for topological maps constituted by nodes and edges in a graph form. The focus is to develop a robust localization algorithm that works well even under various dynamic noises.

For robust localization, the authors propose an algorithm which utilizes all available data such as node information, sensor measurements at the current time step (which are used in previous algorithms) and edge information, and sensor measurements at previous time steps (which have not been considered in other papers). Also, the algorithm estimates a robot's location in a multi‐modal manner which increases its robustness.

Findings show that the proposed algorithm works well in topological maps with various dynamics which are induced by the moving objects in the map and measurement noises from cheap sensors.

Unlike previous approaches, the proposed algorithm has three key features: usage of edge data, inclusion of history information, and a multi‐modal based approach. By virtue of these features, the paper develops an algorithm that enables robust localization performance.

The present research aims to identify determinants for citizen’ behavioural adoption of e-government, explore relationships among these variables and investigate whether the proposed model can provide a more comprehensive manner to understand the adoption of e-government.

First, a survey is administered to collect data, then the Cronbach’s alpha is assessed for internal consistency of measurement scales; second, confirmatory factor analysis is conducted to evaluate the measurement model; finally, a structural equation model is used to test the proposed hypotheses and explore the determinants of e-government adoption.

Results indicate that the proposed model is a stable model with powerful explanatory of variation. In addition, some new relationships in the e-government context are found, whose disposition to trust has positive effect on social norms, whereas perceived risk negatively influences perceived behaviour control. Moreover, other key dominants have been investigated.

The findings have enabled us to better understand factors affecting intention and also provided a solid theoretical research model for future study.