A cellular hardware implementation of a spiking neural network with run-time reconfigurable connectivity is presented. It is implemented on a compact custom FPGA board which provides a powerful reconfigurable hardware platform for hardware and software design. Complementing the system, a CPU synthesized on the FPGA takes care of interfacing the network with the external world. The FPGA board an...

In this paper we compare the evolution of simple behaviour patterns for both an individual and a group of simulated physical robots. An evolutionary algorithm with quite general objective function is used to study the ability to develop behaviour patterns such as the maze exploring ability. The group experiments demonstrate the development of collective behaviour where the group members follow ...

The overall aim of this project was to consolidate and build on the achievements of its predecessor, EPSRC grant GR/L20023 “Cognitive Robotics”. The earlier grant had supplied a proof-of-concept for the idea of highlevel robot control based on logic, through the construction of a robot controller that uses sentences of logic as its units of representation and theorem proving steps as its units ...

This paper describes the implementation and modelling of a biologically-inspired collective behaviour. The experiments are concerned with the gathering and clustering of randomly distributed small cylinders. Each experiment has been repeated ve times and carried out in a simulated environment (parametric simulation) and with a group of ten Khepera miniature mobile robots. The simulated and expe...

The advantages and disadvantages of evolving neural control systems for mobile robots using genetic algorithms are investigated. The Khepera robot is trained using the evolutionary neural networks (E“) algorithm for the task of obstacle avoidance. The feasibility of using Q-learning for robot learning is also studied. It is found that Q-learning can be successfully used to train a robot and is ...

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...