  Sampling-based path planning is a widely used method in robotics,
particularly in high-dimensional state space. Among the whole process of the
path planning, collision detection is the most time-consuming operation. In
this paper, we propose a learning-based path planning method that aims to
reduce the number of collision detection. We develop an efficient neural
network model based on Graph Neural Networks (GNN) and use the environment map
as input. The model outputs weights for each neighbor based on the input and
current vertex information, which are used to guide the planner in avoiding
obstacles. We evaluate the proposed method's efficiency through simulated
random worlds and real-world experiments, respectively. The results demonstrate
that the proposed method significantly reduces the number of collision
detection and improves the path planning speed in high-dimensional
environments.
