  Robot navigation in dynamic environments shared with humans is an important
but challenging task, which suffers from performance deterioration as the crowd
grows. In this paper, multi-subgoal robot navigation approach based on deep
reinforcement learning is proposed, which can reason about more comprehensive
relationships among all agents (robot and humans). Specifically, the next
position point is planned for the robot by introducing history information and
interactions in our work. Firstly, based on subgraph network, the history
information of all agents is aggregated before encoding interactions through a
graph neural network, so as to improve the ability of the robot to anticipate
the future scenarios implicitly. Further consideration, in order to reduce the
probability of unreliable next position points, the selection module is
designed after policy network in the reinforcement learning framework. In
addition, the next position point generated from the selection module satisfied
the task requirements better than that obtained directly from the policy
network. The experiments demonstrate that our approach outperforms
state-of-the-art approaches in terms of both success rate and collision rate,
especially in crowded human environments.
