In this paper, a new method was proposed for the navigation of a mobile robot in an unknown dynamic environment. The robot could detect only a limited radius of its surrounding with its sensors and it went on the shortest null space (SNS) toward the goal. In the case of no obstacle, SNS was a direct path from the robot to goal; however, in the presence of obstacles, SNS was a space around the r...

Long-term human-robot interaction, especially in the case of humanoid robots, requires an adaptable and varied behavior base. In this work we present a method for capturing, or learning, sequential tasks by transferring serial behavior execution from deliberative to routine control. The incorporation of this approach leads to natural development of complex and varied behaviors, with lower deman...

Visual attention refers to the ability of a vision system to rapidly detect visually salient locations in a given scene. On the other hand, the selection of robust visual landmarks of an environment represents a cornerstone of reliable vision-based robot navigation systems. Indeed, can salient scene locations provided by visual attention be useful for robot navigation? This work investigates th...

W e describe techniques to optimally select landmarks in order to perform mobile robot localization by matching terrain maps. The method is based upon a maximum-likelihood robot localization algorithm that efficiently searches the space of possible robot positions. W e u s e a sensor error model to estimate the probability distribution of the terrain expected to be seen from the current robot p...

Behavior-based systems are very useful in making robots adapt to the dynamics of real-world environments. To make these systems more adaptable to various situations and goals to pursue in the world, an interesting idea is to dynamically select behaviors that control the actions of the system. One factor that can give a lot of information about the world is the observation of use of behaviors (o...

We describe techniques to optimally select landmarks for performing mobile robot localization by matching terrain maps. The method is based upon a maximum-likelihood robot localization algorithm that efficiently searches the space of possible robot positions. We use a sensor error model to estimate a probability distribution over the terrain expected to be seen from the current robot position. ...