[Foraging Strategies Application to Robotics]

Multi-agent systems AI Project: Designing Robot Swarms
Date: Mar 2013

DOWNLOAD PAPER
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.

Proposed foraging strategies in different tasks

This paper reviews how food acquisition is solved by various biological species including ants, termites, bees, hyenas, wolves, lions, dolphins, whales and humans. 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.

It is proposed that careful consideration of a foraging task can both increase a robotic swarm's efficiency and make its implementation costs more reasonable.



{Please enable JavaScript in order to post comments}

The Centralised Mindset and Complexity Science

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

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

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.

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.