Bio-Inspired Topological Autonomous Navigation with Active Inference in Robotics

TL;DR

This paper presents a bio-inspired autonomous navigation system using Active Inference that creates real-time topological maps, enabling adaptable exploration and goal-reaching without pre-training.

Contribution

It introduces a novel probabilistic reasoning framework within Active Inference for interpretable, adaptable, and real-time topological navigation in robotics.

Findings

Successfully explored large-scale simulated environments

Adapted to dynamic obstacles and environmental drift

Comparable performance to existing exploration strategies

Abstract

Achieving fully autonomous exploration and navigation remains a critical challenge in robotics, requiring integrated solutions for localisation, mapping, decision-making and motion planning. Existing approaches either rely on strict navigation rules lacking adaptability or on pre-training, which requires large datasets. These AI methods are often computationally intensive or based on static assumptions, limiting their adaptability in dynamic or unknown environments. This paper introduces a bio-inspired agent based on the Active Inference Framework (AIF), which unifies mapping, localisation, and adaptive decision-making for autonomous navigation, including exploration and goal-reaching. Our model creates and updates a topological map of the environment in real-time, planning goal-directed trajectories to explore or reach objectives without requiring pre-training. Key contributions include a probabilistic reasoning framework for interpretable navigation, robust adaptability to dynamic changes, and a modular ROS2 architecture compatible with existing navigation systems. Our method was tested in simulated and real-world environments. The agent successfully explores large-scale simulated environments and adapts to dynamic obstacles and drift, proving to be comparable to other exploration strategies such as Gbplanner, FAEL and Frontiers. This approach offers a scalable and transparent approach for navigating complex, unstructured environments.