PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms
This provides a tool for researchers and developers in robotics to standardize evaluation, though it is incremental as it builds on existing algorithms without introducing new ones.
The authors tackled the lack of a unified platform for benchmarking path planning algorithms in robotics by developing PathBench, which supports both classical and learning-based methods, and demonstrated its use by comparing algorithms on metrics like path length and success rate.
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.