  Sampling-based path planning is a popular methodology for robot path
planning. With a uniform sampling strategy to explore the state space, a
feasible path can be found without the complex geometric modeling of the
configuration space. However, the quality of initial solution is not guaranteed
and the convergence speed to the optimal solution is slow. In this paper, we
present a novel image-based path planning algorithm to overcome these
limitations. Specifically, a generative adversarial network (GAN) is designed
to take the environment map (denoted as RGB image) as the input without other
preprocessing works. The output is also an RGB image where the promising region
(where a feasible path probably exists) is segmented. This promising region is
utilized as a heuristic to achieve nonuniform sampling for the path planner. We
conduct a number of simulation experiments to validate the effectiveness of the
proposed method, and the results demonstrate that our method performs much
better in terms of the quality of initial solution and the convergence speed to
the optimal solution. Furthermore, apart from the environments similar to the
training set, our method also works well on the environments which are very
different from the training set.
