Topological Navigation Graph Framework

TL;DR

This paper introduces a topological navigation graph framework that uses neural networks and imitation learning for goal-directed robot navigation in complex environments.

Contribution

It presents a novel topological navigation graph framework combining neural classifiers and imitation controllers for environment representation and navigation.

Findings

Effective navigation in simulated environments

Successful real-world environment deployment

Utilizes neural object detection for trajectory following

Abstract

We focus on the utilisation of reactive trajectory imitation controllers for goal-directed mobile robot navigation. We propose a topological navigation graph (TNG) - an imitation-learning-based framework for navigating through environments with intersecting trajectories. The TNG framework represents the environment as a directed graph composed of deep neural networks. Each vertex of the graph corresponds to a trajectory and is represented by a trajectory identification classifier and a trajectory imitation controller. For trajectory following, we propose the novel use of neural object detection architectures. The edges of TNG correspond to intersections between trajectories and are all represented by a classifier. We provide empirical evaluation of the proposed navigation framework and its components in simulated and real-world environments, demonstrating that TNG allows us to utilise non-goal-directed, imitation-learning methods for goal-directed autonomous navigation.