We present a novel evolutionary approach to robotic control of a real robot based on genetic programming (GP). Our approach uses genetic programming techniques that manipulate machine code to evolve control programs for robots. This variant of GP has several advantages over a conventional GP system, such as higher speed, lower memory requirements and better real time properties. Previous attemp...

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, nu...

Mobile robots are cyber-physical systems where the cyberspace and the physical world are strongly coupled. Attacks against mobile robots can transcend cyber defenses and escalate into disastrous consequences in the physical world. In this paper, we focus on the detection of active attacks that are capable of directly influencing robot mission operation. Through leveraging physical dynamics of m...

Techniques developed in the fields of evolutionary computation, adaptive systems, agents, and artificial neural networks can be used in entertainment robotics in order to provide easy access to the robot technology. We have developed a number of user-guided approaches based on the techniques from these research fields. These techniques include user-guided behaviour-based systems, user-guided ev...

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...

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 ...

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...

Is it more eecient to use one or several robots? Will the performance of a group of robots working in a collaborative task be enhanced if the robots can communicate with one another? What learning abilities should the robot(s) be provided with for adapting to a continuously changing environment? We address these three issues in a speciic task, namely learning the topography of an environment wh...

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...

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier Systems (CS) have proven their ability of continuos learning, however they have some problems in reactive systems. A modified CS, Reactive Classifier System (RCS), is proposed to overcome those problems. Two special ...