  Path planning plays an important role in autonomous robot systems. Effective
understanding of the surrounding environment and efficient generation of
optimal collision-free path are both critical parts for solving path planning
problem. Although conventional sampling-based algorithms, such as the
rapidly-exploring random tree (RRT) and its improved optimal version (RRT*),
have been widely used in path planning problems because of their ability to
find a feasible path in even complex environments, they fail to find an optimal
path efficiently. To solve this problem and satisfy the two aforementioned
requirements, we propose a novel learning-based path planning algorithm which
consists of a novel generative model based on the conditional generative
adversarial networks (CGAN) and a modified RRT* algorithm (denoted by
CGANRRT*). Given the map information, our CGAN model can generate an efficient
possibility distribution of feasible paths, which can be utilized by the
CGAN-RRT* algorithm to find the optimal path with a non-uniform sampling
strategy. The CGAN model is trained by learning from ground truth maps, each of
which is generated by putting all the results of executing RRT algorithm 50
times on one raw map. We demonstrate the efficient performance of this CGAN
model by testing it on two groups of maps and comparing CGAN-RRT* algorithm
with conventional RRT* algorithm.
