An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots

TL;DR

This paper presents a hybrid approach combining short-term learning with an idiotypic immune network and long-term learning via genetic algorithms to improve mobile robot navigation, demonstrating superior performance over other methods.

Contribution

It introduces a novel integrated LTL-STL architecture using an idiotypic AIS seeded by genetic algorithm-derived behaviors for mobile robot navigation.

Findings

Seeded idiotypic system outperforms STL-only and hand-designed controllers.

Environment differences between LTL and STL phases do not hinder transferability.

The combined approach enhances robot navigation robustness.

Abstract

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability