Topological Navigation Graph
Daniusis, Povilas, Juneja, Shubham, Valatka, Lukas, Petkevicius, Linas
–arXiv.org Artificial Intelligence
In this article, 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.
arXiv.org Artificial Intelligence
Oct-15-2019
- Country:
- North America > United States > New York (0.14)
- Genre:
- Research Report (0.50)
- Industry:
- Automobiles & Trucks (0.93)
- Technology: