Internal representations of the environment are often invoked to explain performance in tasks in which an organism must make a detour around an obstacle to reach a target and the organism can lose sight of the target along the path to the target. By simulating a detour task in evolving populations of robots (Khepera) we show that neural networks with memory units perform better than networks wi...

This paper describes a vision-based system for the autonomous positioning of a mobile robot. Both the pose estimation method and the robot control system are presented. The former finds the position and attitude of the camera mounted on the robot with respect to a known 3D target using a single image. The method uses geometric considerations which combine perspective projection and scaled ortho...

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...

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...

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 ...