The paper presents fuzzy navigation system for a mobile robot. This navigation is based on fuzzy inference machine that performs path planning and obstacle avoidance in arbitrary complex environments. The system is built as two-level hierarchical system. On the higher level there is a controlling PC that is connected with robot control unit through duplex, wireless communication channel. A sens...

To solve the navigation problem of mobile robot in unknown environment, a navigation scheme based on bionic strategy was proposed, which simulates operant conditioning mechanism. In this scheme, the tendency cell was designed by use of information entropy, which represents the tendency degree for state. The improved Q learning algorithm used as learning core to direct the learning direction. Th...

In the present paper a system for generation of topological maps is going to be presented. This system is considered as one of the deliberative skills of the mobile robots architecture named AD. AD is a two level architecture: deliberative and automatic. Those skills which require high computational time as consequence of high level reasoning are found in the deliberative level, while the autom...

Autonomous robots, such as robot office couriers, need control routines that support flexible task execution and effective action planning. This paper describes XFRMLEARN, a system that learns structured symbolic robot action plans for navigation tasks. Given a navigation task, XFRMLEARN learns to structure continuous navigation behavior and represents the learned structure as compact and trans...

In this paper, we explore the issue of incremental learning for autonomous navigation of a mobile robot. The autonomous navigation problem is regarded as a content-based retrieval problem where the robot learns the navigation experience using a hierarchical recursive partition tree (RPT). During real navigation, each time a new image is grabbed to retrieve the learned tree. The associated contr...

The paper presents a hybrid adaptation method that combines a knowledge-based approach with reinforcement learning and a simulated annealing technique, and is applied in the navigation of an educational mobile robot. The experimental results of simulations showed a good behaviour of the robot when doing an adaptive navigation in a dynamic environment by using the proposed hybrid method.

Autonomous robots operating in an unknown and uncertain environment must be able to cope with dynamic changes to that environment. For a mobile robot to navigate successfully to a goal whilst avoiding obstacles is a challenging problem. The mapless navigation technique resembles human behaviours more than other approaches This paper presents a new mapless navigation strategy, based on a multi-s...

This paper presents a 3D navigation subsystem providing speciic treatments needed for the perception and the navigation of an all-terrain mobile robot. This subsystem was developed and integrated in the global framework of the EDEN experimentation. After a brief description of this outdoor navigation experimentation , we describe the natural environment representations we use; two important per...

We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the ‘‘freezing robot problem’’: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers...

Mobile robot navigation in total or partially unknown environments is still an open problem. The path planning algorithms lack completeness and/or performance. Thus, there is the need for complete (i.e., the algorithm determines in finite time either a solution or correctly reports that there is none) and performance (i.e., with low computational complexity) oriented algorithms which need to pe...