[Getting Started with Creeper]



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.

Setup your project

Setup your own project. Typically, your code will be contain in a package that uniquely identifies you and your project. For example, you could set up a package my.name.myProject. This would be an equivalent of creating a directory structure me/name/myproject and pasting your code files withing the myProject directory.

Download Creeper, extract the zip file and copy the 'net' directory (found inside 'src') into the source directory of your own project, so that 'net' and 'my' folders are on the same level. Next, delete directory net/lenkaspace/creeper/creeperDemo - you will not need the demo files.

Controlling Ant-Based Construction

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.

Boid Game-Playing through Randomised Movement

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.

A small compiler script for C with GCC

One of my favourite classes at the moment is the one where they teach us C. Knowing C already, it is a nice relaxation for Monday morning...

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.