  In autonomous robot exploration tasks, a mobile robot needs to actively
explore and map an unknown environment as fast as possible. Since the
environment is being revealed during exploration, the robot needs to frequently
re-plan its path online, as new information is acquired by onboard sensors and
used to update its partial map. While state-of-the-art exploration planners are
frontier- and sampling-based, encouraged by the recent development in deep
reinforcement learning (DRL), we propose ARiADNE, an attention-based neural
approach to obtain real-time, non-myopic path planning for autonomous
exploration. ARiADNE is able to learn dependencies at multiple spatial scales
between areas of the agent's partial map, and implicitly predict potential
gains associated with exploring those areas. This allows the agent to sequence
movement actions that balance the natural trade-off between
exploitation/refinement of the map in known areas and exploration of new areas.
We experimentally demonstrate that our method outperforms both learning and
non-learning state-of-the-art baselines in terms of average trajectory length
to complete exploration in hundreds of simplified 2D indoor scenarios. We
further validate our approach in high-fidelity Robot Operating System (ROS)
simulations, where we consider a real sensor model and a realistic low-level
motion controller, toward deployment on real robots.
