本文提出的工作涉及在未知的室内环境或部分已知的环境中移动机器人的导航问题。

已经开发了一种基于基本行为组合的未知环境中的导航方法。

这些行为大多数是通过模糊推理系统实现的。所提出的导航器结合了两种类型的避障行为，一种用于凸形障碍物，另一种用于凹形障碍物。使用零阶Takagi–Sugenofuzzy推理系统来生成基本行为，例如“到达无碰撞空间的中间”和“跟随墙”非常简单自然。但是，人们总是会担心，从简单的人类专业知识得出的规则或多或少是次优的。这就是为什么我们尝试自动获取这些规则的原因。使用了一种基于类似反向传播算法的技术，该技术通过最小化成本函数，允许对模糊推理系统的参数进行在线优化。最后一点对于从实验数据中提取一组规则而不求助于任何经验方法尤其重要。

在部分已知的环境中，使用混合方法以利用全局和局部导航策略的优势。这些策略的协调基于模糊推理系统，该系统通过对真实场景和存储场景进行在线比较。行程的计划是由可见性图和A？完成的。算法。一方面，通过虚拟机器人在理论环境中遵循计划的路径，另一方面在实际环境与所存储的局部环境相同时，对真实机器人进行导航，从而实现模糊控制器。

The work presented in this paper deals with the problem of the navigation of a mobile robot either in unknown indoorenvironment 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 ofobstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. The use of zero-order Takagi–Sugenofuzzy 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 humanexpertise are more or less sub-optimal. This is why we have tried to obtain these rules automatically. A technique based on aback-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 theexperimental 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 localnavigation strategies. The coordination of these strategies is based on a fuzzy inference system by an on-line comparisonbetween the real scene and a memorized one. The planning of the itinerary is done by visibility graph and A? algorithm. Fuzzycontrollers are achieved, on the one hand, for the following of the planned path by the virtual robot in the theoretical environmentand, 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. Thegood results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections. ? 2002 ElsevierScience B.V. All rights reserved.