This paper presents two local navigation strategies for multi-robot systems in performing exploration of unknown environments. The strategies are based on the frontier cell for mapping the environment and navigating in it. Additionally, two coordination strategies are used to solve possible conflicts among robots. Key criteria investigated are the efficiency and effectiveness represented by the...

This article presents an experiment which investigates how collaboration in a group of simple reactive robots can be obtained through the exploitation of local interactions. A test-bed experiment is proposed in which the task of the robots is to pull sticks out of the ground —an action which requires the collaboration of two robots to be successful. The experiment is implemented in a physical s...

Survival in the animal realm often depends on the ability to elucidate the potentialities for action offered by every situation. This paper argues that affordance learning is a powerful ability for adaptive, embodied, situated agents, and presents a motivation-driven method for their learning. The method proposed considers the agent and its environment as a single unit, thus intrinsically relat...

Using Khepera Simulator software, we developed an autonomous robot with a simple neural network by applying the skin conductance response of an observer who was watching the movement of the agent. First, we found that the signals were generated when the observer felt that the robot faced a crucial phase, such as hitting a wall. Therefore, we used the signals as errors that were back-propagated ...

The design of behavior generating control structures for real robots acting autonomously in a real and changing environment is a complex task. This is in particular true with respect to the debugging process, the documentation of the encountered behavior, its quantitative analysis and the final evaluation. To successfully implement such a behavior, it is vital to couple the synthesis on a simul...

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...

In this paper we present implementation and experiments of a decentralized cooperative exploration strategy developed by our research group. The exploration strategy is based on the construction of a roadmap of the explored area, with the associate safe region. No task decomposition/allocation is performed. The roadmap is incrementally built through a simple and efficient decentralized cooperat...

CAM-Brain is the model to create neural networks based on cellular automata, and ®nally aims at developing an arti®cial brain. In particular, this model can rapidly evolve the neural networks composed of several thousand modules with special purpose computers such as CAM-8 at MIT and CBM at ATR. This paper attempts to evolve a module of CAM-Brain for the problem to control a mobile robot, espec...

It is considered a difficult task to design a controller from scratch for a robot in a dynamic environment. Evolutionary and genetic algorithms are frequently used to find a solution with desired properties. The evolution is supposed to run on one robot or agent processor. In this article we explore the possibility of dividing the genome among several robots. Robots exchange among each other su...