  Robot path planning is difficult to solve due to the contradiction between
optimality of results and complexity of algorithms, even in 2D environments. To
find an optimal path, the algorithm needs to search all the state space, which
costs a lot of computation resource. To address this issue, we present a novel
recurrent generative model (RGM) which generates efficient heuristic to reduce
the search efforts of path planning algorithm. This RGM model adopts the
framework of general generative adversarial networks (GAN), which consists of a
novel generator that can generate heuristic by refining the outputs recurrently
and two discriminators that check the connectivity and safety properties of
heuristic. We test the proposed RGM module in various 2D environments to
demonstrate its effectiveness and efficiency. The results show that the RGM
successfully generates appropriate heuristic in both seen and new unseen maps
with a high accuracy, demonstrating the good generalization ability of this
model. We also compare the rapidly-exploring random tree star (RRT*) with
generated heuristic and the conventional RRT* in four different maps, showing
that the generated heuristic can guide the algorithm to find both initial and
optimal solution in a faster and more efficient way.
