2 research outputs found
Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
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
and , and was demonstrated on real-world
problems with NASA/JPL-Caltech.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2020,
6 + 1 pages, 3 figures, video available at https://youtu.be/VFdihv8Lq2
Asymptotically Optimal Sampling-Based Motion Planning Methods
Motion planning is a fundamental problem in autonomous robotics that requires
finding a path to a specified goal that avoids obstacles and takes into account
a robot's limitations and constraints. It is often desirable for this path to
also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are an
active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge toward the optimal
solution as computational effort approaches infinity. This survey summarizes
the assumptions behind these popular asymptotically optimal techniques and
provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics,
and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews,
https://www.annualreviews.org/. 25 pages. 2 figure