Modern sampling-based motion planning algorithms typically take between hundreds of
milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom …

Trajectory optimization is a widely used technique in robot motion planning for letting the
dynamics of the system shape and synthesize complex behaviors. Several previous works …

Motion planning against sensor data is often a critical bottleneck in real-time robot control.
For sampling-based motion planners, which are effective for high-dimensional systems such …

Parallel search algorithms harness the multithreading capability of modern processors to
achieve faster planning. One such algorithm is PA* SE (Parallel A* for Slow Expansions) …

This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts
the user from data-distribution and parallelism details and allows them to concentrate on …

Lazy search algorithms have been developed to efficiently solve planning problems in
domains where the computational effort is dominated by the cost of edge evaluation. The …

Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning
methods, has been very successful in solving motion planning problems. Even so, sampling …

Anytime search algorithms are useful for planning problems where a solution is desired
under a limited time budget. Anytime algorithms first aim to provide a feasible solution …

Probabilistic Roadmap Methods (PRMs) are widely used motion planning methods that
sample robot configurations (nodes) and connect them to form a graph (roadmap) …

We present a rapidly exploring-random-tree-based parallel motion planning algorithm that
uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk …