  Sampling-based path planning algorithms usually implement uniform sampling
methods to search the state space. However, uniform sampling may lead to
unnecessary exploration in many scenarios, such as the environment with a few
dead ends. Our previous work proposes to use the promising region to guide the
sampling process to address the issue. However, the predicted promising regions
are often disconnected, which means they cannot connect the start and goal
state, resulting in a lack of probabilistic completeness. This work focuses on
enhancing the connectivity of predicted promising regions. Our proposed method
regresses the connectivity probability of the edges in the x and y directions.
In addition, it calculates the weight of the promising edges in loss to guide
the neural network to pay more attention to the connectivity of the promising
regions. We conduct a series of simulation experiments, and the results show
that the connectivity of promising regions improves significantly. Furthermore,
we analyze the effect of connectivity on sampling-based path planning
algorithms and conclude that connectivity plays an essential role in
maintaining algorithm performance.
