2,051 research outputs found
Analysing the behaviour of robot teams through relational sequential pattern mining
This report outlines the use of a relational representation in a Multi-Agent
domain to model the behaviour of the whole system. A desired property in this
systems is the ability of the team members to work together to achieve a common
goal in a cooperative manner. The aim is to define a systematic method to
verify the effective collaboration among the members of a team and comparing
the different multi-agent behaviours. Using external observations of a
Multi-Agent System to analyse, model, recognize agent behaviour could be very
useful to direct team actions. In particular, this report focuses on the
challenge of autonomous unsupervised sequential learning of the team's
behaviour from observations. Our approach allows to learn a symbolic sequence
(a relational representation) to translate raw multi-agent, multi-variate
observations of a dynamic, complex environment, into a set of sequential
behaviours that are characteristic of the team in question, represented by a
set of sequences expressed in first-order logic atoms. We propose to use a
relational learning algorithm to mine meaningful frequent patterns among the
relational sequences to characterise team behaviours. We compared the
performance of two teams in the RoboCup four-legged league environment, that
have a very different approach to the game. One uses a Case Based Reasoning
approach, the other uses a pure reactive behaviour.Comment: 25 page
Modelo de estratégia e coordenação genérico para sistemas multi-agente
Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Jose Nuno Panelas Nunes LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200
High level coordination and decision making of a simulated robotic soccer team
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
A cooperative architecture based on social insects
In the last two decades, cooperative robotic groups have advanced rapidly; beginning with simple, almost blind box pushing tasks and advancing to the complexity of robocup's autonomous soccer matches. Groups of machines have been employed to build structures, search for targets, mimic insects and enact complex formations with precision and aplomb. The complexity of the tasks accomplished have been both impressive and practical,clearly illustrating the potential power of robotic groups and demonstrating how they may be applied to solve real-world problems.
Building on this success, we have created a software architecture that was intended to remove the robotic agents' dependency on complex communications or detailed task specific information.
By incorporating biological models of stigmergic social insect cooperation into the architecture, we aim to ensure that the robots will be able to cooperate implicitly, without regard to group size and with only a weak dependency on task specific information and group homogeneity. We have conducted preliminary investigations into the design's feasibility by using computer simulations of a simple object passing task. This simple task has enabled us to establish that cooperation is possible using this system. This paper will discuss the system's origins, design and future expansion
Microsoft robotics soccer challenge : movement optimization of a quadruped robot
Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Nuno LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200
Roboskeleton: an architecture for coordinating Robot Soccer agents
SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi-agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework.Publicad
A general architecture for robotic swarms
Swarms are large groups of simplistic individuals that collectively solve disproportionately complex tasks. Individual swarm agents are limited in perception,
mechanically simple, have no global knowledge and are cheap, disposable and fallible. They rely exclusively on local observations and local communications. A swarm has no centralised control.
These features are typifed by eusocial insects such as ants and termites, who construct nests, forage and build complex societies comprised of primitive agents.
This project created the basis of a general swarm architecture for the control of insect-like robots. The Swarm Architecture is inspired by threshold models
of insect behaviour and attempts to capture the salient features of the hive in a closely defined computer program that is hardware agnostic, swarm size indifferent and intended to be applicable to a wide range of swarm tasks.
This was achieved by exploiting the inherent limitations of swarm agents. Individual insects were modelled as a machine capable only of perception, locomotion and manipulation. This approximation reduced behaviour primitives
to a fixed tractable number and abstracted sensor interpretation. Cooperation was achieved through stigmergy and decisions made via a behaviour threshold model.
The Architecture represents an advance on previous robotic swarms in its generality - swarm control software has often been tied to one task and robot configuration. The Architecture's exclusive focus on swarms, sets it apart from
existing general cooperative systems, which are not usually explicitly swarm orientated.
The Architecture was implemented successfully on both simulated and real-world swarms
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