The Application of a Dendritic Cell Algorithm to a Robotic Classifier

TL;DR

This paper explores using the dendritic cell algorithm, inspired by the immune system, for robotic classification, demonstrating its effectiveness with minimal tuning and discussing potential extensions for robotic security applications.

Contribution

It introduces the application of the dendritic cell algorithm to robotic classification and evaluates its performance on a real robot with analysis of parameter effects.

Findings

Algorithm performs well with minimal tuning

Migration threshold affects classification performance

Potential for extension to robotic security tasks

Abstract

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.