Giving robots the ability to learn without the guidance of a researcher is a practical method of developing more adaptable and intelligent systems. Self-Organizing Distinctive-State Abstraction (SODA) is an algorithm that allows a robot to learn routines from from raw sensorimotor inputs through training. The goal of our experiment was to implement SODA on a simulated version of the Khepera II ...

This paper represents a method for mobile robot navigation in environments where obstacles are partially unknown. The method uses a path selection mechanism that creates innovative paths through the unknown environment and learns to use routes that are more reliable. This approach is implemented on Khepera robot and verified against shortest path following by wave transform algorithms. Based on...

This paper evaluates a path selection algorithm for mobile robots in large dynamic environments. The aim of the work is to reduce the risk of collisions and time of path following in cases when the robot repeatedly traverses between predefined target points (e.g. fot transportation or inspection tasks). The algorithm is usable even if very little is known about the environment or if it gets com...

This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such ...

This article describes an integrated environment developed for the evaluation of localization and navigation algorithms for mobile robots. The integrated environment, named KITE, superimposes spatial information obtained from the robot and its sensors, upon an overhead view of the robot’s environment. Using the overhead view as the common coordinate system, KITE creates an ideal test-bed in whi...

Most work in evolutionary robotics used a neural net approach for control of a mobile robot. Genetic programming has mostly been used for computer simulations. We wanted to see if genetic programming is capable to evolve a hierarchical control architecture for simple reactive navigation on a large physical mobile robot. First, we evolved hierarchical control algorithms for a mobile robot using ...

The goal of this work is to propose a learning procedure for fuzzy systems. Fuzzy systems are able to treat uncertain and imprecise informations. They have a capability to express knowledge in the form of linguistic rules. Their drawbacks are caused mainly by the diiculty of deening accurate membership functions and lack of a systematic procedure for the transformation of the expert knowledge i...

A novel robot learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated. PSL is a model-free prediction algorithm inspired by the dynamic temporal difference algorithm S-Learning. While S-Learning has previously been applied as a reinforcement learning algorithm for robots, PSL is here applied to a Learning from Demonstration problem. The proposed algorithm is eval...

This paper presents an emotion based process generating cognition through learning. Two models are described which investigates the possibilities of designing cognitive systems based on emotion. In order to test these models through experiments, we used a mobile robot simulator, Webots, modeling the real Khepera robot. The rst proposed model is the simplest since it uses only positive and negat...