Marlin Strub has had two papers on informed, sampling-based planners accepted to ICRA 2020 in Paris, France. His first is on Advanced Batch Informed Trees (ABIT*) and you can read it right now on arXiv and check out our code page if you would like to try it yourself.
Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approaches are powerful but have limitations.
This paper continues work to combine their strengths and mitigate their limitations using a unified planning paradigm. It does this by viewing the path planning problem as the two subproblems of search and approximation and using advanced graph-search techniques on a sampling-based approximation.
This perspective leads to Advanced BIT*. ABIT* combines truncated anytime graph-based searches, such as ATD*, with anytime almost-surely asymptotically optimal sampling-based planners, such as RRT*. This allows it to quickly find initial solutions and then converge towards the optimum in an anytime manner. ABIT* outperforms existing single-query, sampling-based planners on the tested problems in R4 and R8, and was demonstrated on real-world problems with NASA/JPL-Caltech.