[Multi-agent systems]

Designing Robot Swarms

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

Robustness in Foraging E-puck Swarms Through Recruitment

 Robots Multi-agent systems Swarms of five e-puck robots are used in a semi-virtual environment, facilitated by the VICON...

Information Flow Regulation in Preferentially Foraging Swarms

 Multi-agent systems Swarms are tasked with foraging from multiple sources in dynamic environments where source...

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

 Multi-agent systems AI The Information-Cost-Reward (ICR) framework relates the way in which robots obtain and share...

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

 Multi-agent systems AI Behaviour-Data Relations Modeling Language (BDRML) explicitely represents behaviours and data that...

Task Allocation in Foraging Robot Swarms

 Multi-agent systems C++ Bee-inspired self-regulation algorithms for robot swarms that deliver items into a single drop-off...

Information Flow Principles for Plasticity in Robot Swarms

 Multi-agent systems C++ An important characteristic of a robot swarm that must operate in the real world is the ability to...

Understanding the Role of Recruitment in Robot Foraging

 Multi-agent systems Java AI The ability of a simulated robotic swarm of individualists and a swarm of bee-like communicators to...

Controlling Ant-Based Construction

 Multi-agent systems Java Simulation models Stigmergy allows insect colonies to collectively build structures that no single individual is...

Boid Game-Playing through Randomised Movement

 Multi-agent systems Java AI Simulation models The original boid flocking algorithm is extended by adding randomised movement to the flock...

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.