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[Search by tag: Multi-agent systems]

Designing Robot Swarms

Multi-agent systems Date: 17/08/2018 :: 0 comments

This project looks at the challenges involved in modeling, understanding and designing of multi-robot systems.

Robustness in Foraging E-puck Swarms Through Recruitment

Robots Multi-agent systems Date: 02/08/2018 :: 0 comments

Swarms of five e-puck robots are used in a semi-virtual environment, facilitated by the VICON positioning system. Recruitment can make swarms more robust to noise in robot global positioning data.

Information Flow Regulation in Preferentially Foraging Swarms

Multi-agent systems Date: 05/07/2018 :: 0 comments

Swarms are tasked with foraging from multiple sources in dynamic environments where source locations change periodically. Preferential foraging leads to a better swarm performance, provided that information flow among robots, that results from recruitment, is regulated

V-REP, Gazebo or ARGoS? A robot simulators comparison

Robots Multi-agent systems C++ AI Tutorials Date: 17/01/2018 :: 10 comments

Let’s have a look at three commonly used open-source simulators for robotics: V-REP, Gazebo and ARGoS, to find out which one suits your project the best.

The Information-Cost-Reward framework for understanding robot swarm foraging

Multi-agent systems AI Date: 24/11/2017 :: 0 comments

The Information-Cost-Reward (ICR) framework relates the way in which robots obtain and share information about where work needs to be done to the swarm’s ability to exploit that information in order to perform work efficiently in the context of a particular task and environment.

Behaviour-Data Relations Modelling Language For Multi-Robot Control Algorithms

Multi-agent systems AI Date: 20/09/2017 :: 0 comments

Behaviour-Data Relations Modeling Language (BDRML) explicitely represents behaviours and data that robots utilise, as well as relationships between them. This allows BDRML to express control algorithms where robots cooperate and share information with each other while interacting with the environment.

Robot swarms in action

Multi-agent systems Robots AI Date: 28/06/2017 :: 0 comments

Watch e-puck robots collect resources and bring them back to base. While the previous simulation work helped us to learn a lot about the advantages and disadvantages of communication in swarms, doing similar experiments with real robots is already revealing interesting new things.

How to compile code for e-puck robots on your computer

Multi-agent systems Robots C++ Date: 31/03/2017 :: 2 comments

Compiling code natively on e-puck robots or cross-compiling on your computer can be very tedious. Luckily, there is a third option: compiling code on a virtualised robot system on your computer, then sending the program onto the robot.

Task Allocation in Foraging Robot Swarms

Multi-agent systems C++ A-Life Date: 29/04/2016 :: 0 comments

Bee-inspired self-regulation algorithms for robot swarms that deliver items into a single drop-off location and need to prevent congestion.

Are Robot Swarms Like Brains?

Multi-agent systems Robots Neural networks Mind A-Life Date: 19/02/2016 :: 0 comments

I have recently explored a way of measuring how information flows within a robot swarm. I think that there is something intriguing behind this idea - a swarm's resemblance to the human brain.

Information Flow Principles for Plasticity in Robot Swarms

Multi-agent systems C++ Date: 03/10/2015 :: 0 comments

An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that analysing swarm behaviour in terms of information flow can help us to identify whether a particular behavioural strategy is likely to exhibit useful swarm plasticity in response to dynamic environments.

The Hive Mind in Southampton

Multi-agent systems Robots Games AI A-Life Date: 16/03/2015 :: 0 comments

I had the privilege to present The Hive Mind, my new sci-fi puzzle game about insect-inspired construction with robots, at the University of Southampton Science and Engineering day last weekend. A lot of people came to play it and it was great to watch how they interacted with it - kids were often better at the game than their parents!

How will artificial life impact the future?

Multi-agent systems Robots Neural networks AI A-Life Date: 26/10/2014 :: 0 comments

In 2013, I was a part of the TRUCE workshop at the Alife conference. The workshop brought together scientists and creative writers in order to create a cool book full of stories about A-life (artificial life) and artificial intelligence and about how it will impact our society in the future. As I am very interested in swarm robotics, sci-fi games and movies and generally how the future will look like when robots run around and are part of our everyday lives, I jumped at the opportunity to cooperate on the project.

Impressions from ALIFE 14 New York

Multi-agent systems Robots AI A-Life Date: 12/08/2014 :: 0 comments

This summer, I attended the Artificial Life conference in New York. There were some interesting and not-so-interesting talks, but generally I am very glad I went. I had a chance to meet some great people and more importantly, to get much needed feedback on my own research. I also got offered to try out real robots in my research.

Creeper

Multi-agent systems Libraries Java Games AI A-Life Date: 18/09/2013 :: 0 comments

Creeper is a Java MVC framework for those who want to create multi-agent simulations (or games) and need something to build on. Creeper takes care of effective updating and rendering. You only need to specify the world objects and how they should look like.

Impressions from ECAL 2013

Multi-agent systems Robots A-Life Date: 17/09/2013 :: 2 comments

I recently returned from Taormina, Sicily where I attended the ECAL 2013 conference. It was so amazing that I felt I had to share my experience from it on this blog.

Understanding the Role of Recruitment in Robot Foraging

Multi-agent systems Java AI Date: 16/09/2013 :: 0 comments

The ability of a simulated robotic swarm of individualists and a swarm of bee-like communicators to collect resources from various environments is compared.

Accepted to the ECAL 2013 A-Life conference

Multi-agent systems Robots A-Life Date: 22/06/2013 :: 1 comment

The paper 'Controling Ant-Based Construction' that I recently wrote in cooperation with my supervisor Seth Bullock has now been accepted to the ECAL 2013 conference. The work is about a simulation of 2D ant nest building, where different nest shapes are made...

Stardust Colonies Web Site Is Launched!

Multi-agent systems Games C++ AI Date: 31/05/2013 :: 0 comments

I have finally launched web site for my strategy game Stardust Colonies. Unlike in most strategy games, you get to control minions who have their job preferences and will behave differently based on what you tell them to do. Also, you don't train units in buildings but clone existing ones, which I think feels more natural to the player...

Getting Started with Creeper

Tutorials Multi-agent systems Libraries Java Games AI A-Life Date: 27/05/2013 :: 0 comments

This tutorial helps you to create your own Java project with Creeper. As an alternative solution, you could simply download Creeper with the Demo project and rewrite class and package names within net.lenkaspace.creeper.demo to suit your own needs.

Controlling Ant-Based Construction

Multi-agent systems Java A-Life Simulation models Date: 02/04/2013 :: 0 comments

Stigmergy allows insect colonies to collectively build structures that no single individual is fully aware of. Since relatively minimal sensory and reasoning capabilities are required of the agents, such building activity could be utilised by robotic swarms if we could learn how to control the shape of the final structures.

Foraging Strategies in Nature and Their Application to Swarm Robotics

Multi-agent systems AI A-Life Date: 22/03/2013 :: 0 comments

While foraging is a task often experimented with in swarm robotics, it is often the case that foraging strategies inspired by nature are chosen without careful consideration. Foraging strategies including solitary foraging, behavioural matching, stigmergy, signaling to guide others and coordinated and cooperative hunting are identified and their implementation costs in robots, as well as their suitability for different scenarios is discussed.

The 'I Matter' Illusion

Multi-agent systems Mind Date: 24/02/2013 :: 2 comments

How many times a day do you encounter slogans like 'treat yourself with this and that product' or 'your choice of this and that' or even 'this and that is in this way for your convenience'? The idea that the world is all about you creeps everywhere these days...

The Centralised Mindset and Complexity Science

Multi-agent systems Mind Date: 05/01/2013 :: 0 comments

Humans tend to explain decentralised phenomena as being caused by a single entity. This way of thinking is often referred to as 'the centralised mindset'. Several authors propose that using programming environments where creation of decentralised agent-based systems is easy...

Boid Game-Playing through Randomised Movement

Multi-agent systems Java AI A-Life Simulation models Date: 22/10/2012 :: 0 comments

The original boid flocking algorithm is extended by adding randomised movement to the flock members. This approach is a light-weight alternative to other ‘follow the leader’ techniques implemented in order to create a ‘game-playing’ behaviour during which a flock changes its movement direction as observed in real birds.

Starting my PhD Next Week

Multi-agent systems Robots Date: 16/09/2012 :: 0 comments

I have finally finished making my studio in Southmapton at least decently homey as I am starting my PhD programme on Tuesday. I met some of my classmates yesterday (Saturday) during a barbeque and I am very excited about our future discussions and beer drinking.

My First Journal Publication

Multi-agent systems Robots Neural networks A-Life Date: 06/09/2012 :: 0 comments

After working on it for quite a long time, my paper titled Ultrastable Neuroendocrine Robot Controller was finally accepted for publication in Adaptive Behaviour.

To Vote or Not To Vote: A Swarm Approach

Multi-agent systems Mind Date: 19/01/2012 :: 0 comments

I’ve never voted. I am not sure why, I perhaps don't care enough. People always tell me: 'Imagine everybody would decide not to vote! You have to vote because every small opinion counts towards the final results'. I've never agreed with it, although I couldn't express why...

Does Communication Make a Difference?

Multi-agent systems Date: 19/04/2010 :: 0 comments

This paper compares different animal groups from eusocial insect colonies to human society and discusses their mechanics and behaviour as agent systems. The main focus is on interaction between the agents and on how properties of a system like effectiveness or predictability are affected by these interactions.

Robot Flocking: Sensors and Control

Multi-agent systems AI Date: 04/01/2010 :: 0 comments

This paper discusses various kinds of robot sensory input, approaches to motor control and ways they could be used for flocking. Focus is put on vision and Gibsonian optic flow that could be utilised by robots with advanced behaviour.

pyCreeper

The main purpose of pyCreeper is to wrap tens of lines of python code, required to produce graphs that look good for a publication, into functions. It takes away your need to understand various quirks of matplotlib and gives you back ready-to-use and well-documented code.

Novelty detection with robots using the Grow-When-Required Neural Network

The Grow-When-Required Neural Network implementation in simulated robot experiments using the ARGoS robot simulator.

Fast Data Analysis Using C++ and Python

C++ code that processes data and makes it available to Python, significantly improving the execution speed.

Designing Robot Swarms

This project looks at the challenges involved in modeling, understanding and designing of multi-robot systems.

Robustness in Foraging E-puck Swarms Through Recruitment

Swarms of five e-puck robots are used in a semi-virtual environment, facilitated by the VICON positioning system. Recruitment can make swarms more robust to noise in robot global positioning data.