  Path planning is an essential component of mobile robotics. Classical path
planning algorithms, such as wavefront and rapidly-exploring random tree (RRT)
are used heavily in autonomous robots. With the recent advances in machine
learning, development of learning-based path planning algorithms has been
experiencing rapid growth. An unified path planning interface that facilitates
the development and benchmarking of existing and new algorithms is needed. This
paper presents PathBench, a platform for developing, visualizing, training,
testing, and benchmarking of existing and future, classical and learning-based
path planning algorithms in 2D and 3D grid world environments. Many existing
path planning algorithms are supported; e.g. A*, Dijkstra, waypoint planning
networks, value iteration networks, gated path planning networks; and
integrating new algorithms is easy and clearly specified. The benchmarking
ability of PathBench is explored in this paper by comparing algorithms across
five different hardware systems and three different map types, including
built-in PathBench maps, video game maps, and maps from real world databases.
Metrics, such as path length, success rate, and computational time, were used
to evaluate algorithms. Algorithmic analysis was also performed on a real world
robot to demonstrate PathBench's support for Robot Operating System (ROS).
PathBench is open source.
