  Path planning is a key component in mobile robotics. A wide range of path
planning algorithms exist, but few attempts have been made to benchmark the
algorithms holistically or unify their interface. Moreover, with the recent
advances in deep neural networks, there is an urgent need to facilitate the
development and benchmarking of such learning-based planning algorithms. This
paper presents PathBench, a platform for developing, visualizing, training,
testing, and benchmarking of existing and future, classical and learned 2D and
3D path planning algorithms, while offering support for Robot Oper-ating System
(ROS). Many existing path planning algorithms are supported; e.g. A*,
wavefront, rapidly-exploring random tree, value iteration networks, gated path
planning networks; and integrating new algorithms is easy and clearly
specified. We demonstrate the benchmarking capability of PathBench by comparing
implemented classical and learned algorithms for metrics, such as path length,
success rate, computational time and path deviation. These evaluations are done
on built-in PathBench maps and external path planning environments from video
games and real world databases. PathBench is open source.
