M. Sempere

Agents for Swarm Robotics: Architecture and Implementation

Swarm robotics is a type of robotic systems based on many simple robots interactions. Such systems enjoy many benefits such as high tolerance and the possibility of increasing the number of robots in a transparent way to the programmer; but they also have many difficulties when applied to complex problems. In this paper, we will present a hybrid architecture for swarm robotics based on a multi-agent system. The main contribution of this architecture is to make possible the use of cognitive agents to lead a robotic swarm of simple agents without losing the advantages of swarms. Moreover, the implementation of this architecture within Real Swarm platform and the discussion of how to apply this architecture in real systems will be presented.

On Cooperative Swarm Foraging for Simple, Non Explicitly Connected, Agents

Nowadays there are several applications that use swarm robotics for solving research tasks and resource exploitation. Most of these applications are based on complex agents that require explicit communication between them. These systems are difficult to introduce in certain environments because of these features, where agents can not always communicate between them and where it would be necessary a large swarm. This paper presents a swarm system for a collective resource exploitation. The main features of the agents of this system are their simplicity and they do not communicate with each other in explicit way. A microscopic model that shows the individual performance of agents has been proposed, and a macroscopic model that describes the overall swarm system has been provided. Several tests that show the convergence of the swarm towards the best resource in an unknown environment have been analyzed.

A cognitive model for autonomous agents based on bayesian programming

This paper presents a cognitive model for an autonomous agent based on emotional psychology and Bayesian programming. A robot with emotional responses allows us to plan behaviour in a different way than present robotic architectures and provides us with a method of generating a new interface for human/robot interaction. The use of emotional modules means that the emotional state of the robot can be obtained directly and, therefore, it is relatively simple to obtain a virtual face that represents these emotions. An autonomous agent could have a model of the environment to be able to interact with the real universe where it is working. It is necessary to consider that any model of a real phenomenon will be incomplete due to the existence of uncertain, unknown variables that influence the phenomenon. Two example arquitectures are proposed here. Using these architectures some experimental data, to verify the correctness of this approach, is provided.

A macroscopic model for high intensity radiofrequency signal detection in swarm robotics systems

In recent years, there has been a growing interest in resource location in unknown environments for robotic systems, which are composed of multiple simple robots rather than one highly capable robot [M. Sempere, F. Aznar, M. Pujol, and R. Rizo, On cooperative swarm foraging for simple, non explicitly connected, agents, 2010]. This tradeoff reduces the design and hardware complexity of the robots and removes single point failures, but adds complexity in algorithm design. The challenge is to programme a swarm of simple robots, with minimal intercommunication and individual capability, to perform a useful task as a group. This paper is focused on finding the highest intensity area of a radiofrequency (RF) signal in urban environments. These signals are usually more intense near the city centre and its proximity, since in these zones the risk of signal saturation is high. RF radiation (RFR) is boosted or blocked mainly depending on orography or building structures. RF providers need to supply enough coverage, setting up different antennas to be able to provide a minimum quality of service. We will define a micro/macroscopic mathematical model to efficiently study a swarm robotic system, predict their long-term behaviour and gain insight into the system design. The macroscopic model will be obtained from Rate Equations, describing the dynamics of the swarm collective behaviour. In our experimental section, the Campus of the University of Alicante will be used to simulate our model. Three RFR antennas will be taken into account, one inside our Campus and the other two in its perimeter. Several tests, that show the convergence of the swarm towards the RFR, will be presented. In addition, the obtained RFR maps and the macroscopic behaviour of the swarm will be discussed.

On intelligent interface agents for human based computation

In this paper a new type of interface agent will be presented. This agent is oriented to model systems for human based computation. This kind of computation, that we consider a logical extension of intelligent agent paradigm, emerges as valid approach for the resolution of complex problems. Firstly an study of the state of the art of interface agents will be review. Next, human based computation will be defined and we will see how is necessary to extend the current typology of interface agents to model this new kind of computation. In addition, a new type of interface agent, oriented to model this type of computational system, will be presented. Finally, two of the most representative applications of human based computation will be specified using this new typology.

Positioning of Geometric Formations in Swarm Robotics

Nowadays, swarm robotics is presented as a solution for the collaboration between agents whose goal is the resolution of a common objective. One of the major challenges involved in the design of these algorithms is that they are expected to be distributed, scalable and fault tolerant. In this paper, we present an algorithm that, from random positions, gathers all the agents in an established geometric formation. This common formation is a preliminary step to teamwork and to achieve a common objective. The presented algorithm is distributed, scalable and fault tolerant.

Bayesian emotions: developing an interface for robot/human communication

This paper presents a fusion model of robotic behaviour based on emotional psychology. The main purpose of this model is to provide a human interface that represents the present state of the robot. This interface has two main advantages, firstly it can easily be understood by non-computer experts, and secondly its use is independent of language. The use of emotional modules means that the emotional state of the robot can be obtained directly and, therefore, it is relatively simple to obtain a virtual face that represents these emotions. In addition, the model proposed here, is defined as a complement to the present robotic models. Some experimental data, to verify the correctness of this approach, is provided.

3D robot mapping: combining active and non active sensors in a probabilistic framework

Map reconstruction and robot location are two essential problems in the field of robotics and artificial intelligence. A robot could need a model of the environment that can be incomplete and therefore the robot must work considering the uncertainty. Bayesian Units consider the uncertainty and allow the fusion of information from different sensors. In this paper a map reconstruction system in 3D based on Bayesian Units is presented. The reconstruction is carried out integrating the data obtained by a laser and by an omnivision system. In addition, to improve the quality of the reconstruction, the fusion of several Bayesian Units is defined using a competitive fusion operator. Finally, the obtained results as well as the validity of the system are shown.