Topological Navigation Graph Framework
This addresses navigation challenges for mobile robots in complex environments, though it appears incremental as it builds on existing imitation-learning and neural network techniques.
The paper tackles the problem of goal-directed mobile robot navigation in environments with intersecting trajectories by proposing a topological navigation graph framework that represents environments as directed graphs of neural networks, demonstrating that this approach enables the use of non-goal-directed imitation-learning methods for autonomous navigation.
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.