This paper describes the implementation of two biologically inspired collective behaviours on a group of Khepera miniature mobile robots. The rst experiment is concerned with the gathering and clustering of randomly distributed small cylinders. In the second experiment the group of robots are expected to remove long sticks from holes, requiring a synchronous collaboration between two robots. Th...

Nolfi in [1] presented an experimental comparison of weight evolution in five different neural network architectures (feed-forward vs. recurrent, modular vs. non-modular) for the control of a Khepera robot, which had to pick up objects with its gripper arm and place them outside an arena. Best results were achieved with a so-called emergent modular architecture. This paper extends Nolfi’s exper...

In this paper, we use a massive modular architecture for the generation of complex behaviours in complex robots within the evolutionary robotics framework. We define two different ways of introducing modularity in neural controllers using evolutionary techniques, which we call strategic and tactical modularity, and show at what modular levels each one acts and how can they be combined for the g...

Roborobo! is a multi-platform, highly portable, robot simulator for large-scale collective robotics experiments. Roborobo! is coded in C++, and follows the KISS guideline (”Keep it simple”). Therefore, its external dependency is solely limited to the widely available SDL library for fast 2D Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast single and multi-robots si...

In this paper we develop two time-invariant control laws for a unicycle-type mobile robot. A mobile robot of this type is an example of a system with a nonholonomic constraint. Similarly to the majority of results in the literature thus far, the controllers are based on the robot's kinematic model. They do not directly address realistic factors such as motor dynamics, quantization, sensor noise...

We present the results of a research aimed at improving the Q-learning method through the use of artificial neural networks. Neural implementations are interesting due to their generalisation ability. Two implementations are proposed: one with a competitive multilayer perceptron and the other with a self-organising map. Results obtained on a task of learning an obstacle avoidance behaviour for ...

An evolutionary algorithm for the creation of recurrent network structures is presented. The aim is to develop neural networks controlling the behaviour of miniature robots. Two diierent tasks are solved with this approach. For the rst, the agents are required to move within an environment without colliding with obstacles. In the second task, the agents are required to move towards a light sour...

In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior based navigation and action sequence based environ...

We describe a hippocampal neural model in which spatio-temporal features of the environment are extracted by visually driven neu-rons. The neuronal ring activity implicitly measures properties like agent-landmark distance and egocentric orientation to visual cues. This leads to a neural representation where populations of place cells encode spatial locations within the environment. In addition,...

We have used an automatic programming method called genetic programming (GP) for control of a miniature robot. Our earlier work on real-time learning suffered from the drawback of the learning time being limited by the response dynamics of the robot's environment. In order to overcome this problem we have devised a new technique which allows learning from past experiences that are stored in mem...