A generic infrastructure for benchmarking motion planners

Randomized planners, search-based planners, potential-field approaches and trajectory optimization based motion planners are just some of the types of approaches that have been developed for motion planning. Given a motion planning problem, choosing the appropriate algorithm to use is a daunting task even for experts since there has been relatively little effort in comparing the plans generated by the different approaches, for different problems. In this paper, we present a set of benchmarks and the associated infrastructure for comparing different types of motion planning approaches and algorithms. The benchmarks are specifically designed for robotics and include typical indoor human environments. We present example motion planning problems for single arm tasks. Our infrastructure is designed to be easily extensible to allow for the addition of new planning approaches, new robots, new environments and new metrics. We present results comparing the performance of several motion planning algorithms to validate the use of these benchmarks.