Sensor-based navigation of a mobile robot in an indoor environment

The work presented in this paper deals with the problem of the navigation of a mobile robot either in unknown indoor environment or in a partially known one. A navigation method in an unknown environment based on the combination of elementary behaviors has been developed. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. The use of zero-order Takagi–Sugeno fuzzy inference systems to generate the elementary behaviors such as “reaching the middle of the collision-free space” and “wall-following” is quite simple and natural. However, one can always fear that the rules deduced from a simple human expertise are more or less sub-optimal. This is why we have tried to obtain these rules automatically. A technique based on a back-propagation-like algorithm is used which permits the on-line optimization of the parameters of a fuzzy inference system, through the minimization of a cost function. This last point is particularly important in order to extract a set of rules from the experimental data without having recourse to any empirical approach. In the case of a partially known environment, a hybrid method is used in order to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system by an on-line comparison between the real scene and a memorized one. The planning of the itinerary is done by visibility graph and A∗ algorithm. Fuzzy controllers are achieved, on the one hand, for the following of the planned path by the virtual robot in the theoretical environment and, on the other hand, for the navigation of the real robot when the real environment is locally identical to the memorized one. Both the methods have been implemented on the miniature mobile robot Khepera® that is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections. © 2002 Elsevier Science B.V. All rights reserved.