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ANUBIS: artificial neuromodulation using a Bayesian inference system.

Benjamin J H Smith¹, Chakravarthini M Saaj, Elie Allouis

¹Surrey Space Centre, Department of Electronic Engineering, University of Surrey, Guildford, Surrey, UK. ee52bs@surrey.ac.uk

Neural Computation

|September 14, 2012

Summary

This summary is machine-generated.

This study introduces ANUBIS, a novel Bayesian brain-inspired system for real-time controller tuning. ANUBIS enhances robot adaptability and efficiency, outperforming traditional methods in simulations.

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Area of Science:

Robotics and Control Systems
Computational Neuroscience
Artificial Intelligence

Background:

Controller design requires accurate system understanding and prediction of operational disturbances.
Existing intelligent tuning techniques often lack mathematical interpretability.

Purpose of the Study:

To present ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatic, real-time controller parameter tuning.
To evaluate ANUBIS's performance in enhancing controller efficiency and adaptability.

Main Methods:

ANUBIS is based on the Bayesian brain concept, incorporating a neuromodulatory system with four artificial neuromodulators.
Implemented in a prototype walking rover (EchinoBot) for gait generation, foot trajectory planning (Bézier curves), and trajectory tracking (sliding mode control).
Compared ANUBIS against a multilayer perceptron (MLP) tuned system.

Main Results:

ANUBIS demonstrated significant improvements in efficiency and adaptability across the three controller components.
The ANUBIS-tuned robot reacted faster to obstacles and uncertainties than the MLP-tuned system.
Stability and accuracy were maintained, showcasing ANUBIS's robust performance.

Conclusions:

ANUBIS offers a mathematically interpretable alternative to conventional intelligent tuning methods.
The system advances rover autonomy and shows potential for applications like process control with changing operating conditions.
Demonstrates a viable integration of neuromodulation within the Bayesian brain framework.