Log In Sign Up

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

by   Amanda Whitbrook, et al.

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


Two-Timescale Learning Using Idiotypic Behaviour Mediation For A Navigating Mobile Robot

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approa...

FCLT - A Fully-Correlational Long-Term Tracker

We propose FCLT - a fully-correlational long-term tracker. The two main ...

Curating Long-term Vector Maps

Autonomous service mobile robots need to consistently, accurately, and r...

Robot Safe Interaction System for Intelligent Industrial Co-Robots

Human-robot interactions have been recognized to be a key element of fut...

Controlling Smart Propagation Environments: Long-Term versus Short-Term Phase Shift Optimization

Reconfigurable intelligent surfaces (RISs) have recently gained signific...

The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms

This paper demonstrates that a set of behaviours evolved in simulation o...